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FP7
-
ICT
-
2011
-
9


STREP
proposal

18/01/
12

v1


SHAPEATLAS

Proposal Part B: page

1



Small or medium
-
scale f
ocused research project (STREP
)



ICT Call 9

FP7
-
ICT
-
2011
-
9


SHAPEATLAS


Type of project:


Small or medium scale focused research project (STREP
)


Date of
preparation
:


April 17
th
, 2012


Work programme objective addressed
:


Objective ICT
-
2011.8.2 ICT for access to cultural resources



Name of the coordinating person
:


Torsten Ullrich


e
-
mail
:


torsten.ullrich@fraunhofer.at




fax:


+43 316 873 105404


Participant no.

Participant organisation name

Part. short name

Country

1 (Coordinator)

Fraunhofer Austria Research GmbH

FhA

Austria

2

University of Brighton

UOB

United Kingdom

3

Technische Universität Graz

TUG

Austria

4

K
atholieke
U
niversiteit
L
euven

KUL

Belgium

5

Brunswick Town Charitable Trust

BTCT

United Kingdom


Proposal abstract

The SHAPEATLAS project will advance the state of the art in the access to cultural resources, in particular
to large collections of similar 3D shapes, for public,
research, and education. Searching in collections is a
delicate task. Museums typically collect many objects of the same kind, e.g., coins, swords, vases, statues,
etc. Art historians are typically interested in just a few subtle properties of a given shap
e which they use for
various hypotheses, e.g., about the era, the location of origin, about cultural influences, or the diffusion of
technology. There are many examples of shape typologies, e.g., of columns, of helmets, or of amphorae
(Dressel typology wit
h 66 types). Similarity measures that are based on machine learning treat shape
description and classification as a purely statistical problem (clustering of feature vectors etc.). Such methods
fail in distinguishing subtle differences of objects in a coll
ection. And, more importantly, there is no way for
users to explicitly specify what to look for. Small shape differences can lead to a very different classification.



The SHAPEATLAS project will introduce an innovative concept to solve the problem: parame
tric shape
maps. A shape map is basically a user
-
defined shape measurement tool. It can adapt to a given object
(scanned sword) to measure its characteristic parameters (blade length and width). Since a shape can be
measured in different ways, several shap
e maps can exist in parallel. SHAPEATLAS will investigate
methods to define and to apply shape maps interactively, but also automatically, e.g., to classify a larger
collection overnight. Novel searching and browsing tools will allow searching for statisti
cal and for
parametric shape properties, thus combining both approaches. The project results will be widely applicable,
which will be demonstrated with the use case of building restoration (large number of moulds) and museum
collection management.

FP7
-
ICT
-
2011
-
9


STREP
proposal

18/01/
12

v1


SHAPEATLAS

Proposal Part B: page

2


Table of

Contents


1

Scientific and/or technical quality, relevant to the topics addressed by the call

..........................

3


Concept and objectives

................................
................................
................................
..........

3

1.1

Progress beyond the state
-
of
-
the
-
art

................................
................................
......................

9

1.2

S/T methodology and associated work plan

................................
................................
........

11

1.3
1.3.1

Work package list
................................
................................
................................
.........

11

1.3.2

List of deliverables

................................
................................
................................
.......

11

1.3.3

List of milestones

................................
................................
................................
.........

12

1.3.4

Work package

description

................................
................................
............................

13

1.3.5

Summary of effort

................................
................................
................................
........

21

2

Implementation

................................
................................
................................
..........................

22


Management structure and procedures

................................
................................
................

22

2.1
2.1.1

Management roles

................................
................................
................................
........

22

2.1.
2

Decision
-
making bodies and mechanisms

................................
................................
...

22

2.1.3

Reporting mechanisms

................................
................................
................................
.

22

2.1.4

Meetings, conferencing and communication tools

................................
......................

23

2.1.5

Knowledge management and intellectual property rights

................................
............

23

2.1.6

Quality assurance and control

................................
................................
......................

23

2.1.7

Conflict management

................................
................................
................................
...

23


Individual participants

................................
................................
................................
........

24

2.2
2.2.1

Fraunhofer Austria Research GmbH

................................
................................
............

24

2.2.2

University of Brighton

................................
................................
................................
.

25

2.2.3

Technische Universität Graz

................................
................................
........................

26

2.2.4

Katholieke Universiteit Leuven

................................
................................
...................

27

2.2.5

Brunswick Town Charitable Trust

................................
................................
...............

28


Consortium as a whole

................................
................................
................................
.......

28

2.3

Resources to be committed
................................
................................
................................
.

28

2.
4
3

Impact
................................
................................
................................
................................
.........

29


Expected impacts listed in the work programme

................................
................................

29

3.1

Dissemination and/or exploitation of project results, and management of intellectual
3.2
property

................................
................................
................................
................................
..........

29

4

Ethical Issues
................................
................................
................................
..............................

30

5

References

................................
................................
................................
................................
..

31




FP7
-
ICT
-
2011
-
9


STREP
proposal

18/01/
12

v1


SHAPEATLAS

Proposal Part B: page

3


1

Scientific and/or technical quality, relevant to the topics addressed by the
call




Concept and
objectives

1.1
The SHAPEATLAS project will greatly advance the state of the art in the access to cultural
resources, especially to very large collections of 3D shapes, for public, research, and education. The
availability of 3D
-
representations of cultural
artefacts

must
become a standard in all sectors that are
concerned with cultural
artefacts
. In the near future, whenever a specific cultural object is
mentioned, there will be the expectation that a 3D
-
representation of it is available; that various other
similar objects

can be obtained through 3D searching and browsing; and that subtle shape variations
are detected and highlighted. Especially important is the case of collections of many similar objects.
It is rarely the case that a museum will host only one object of a k
ind. Instead, museums typically
collect many coins, or many swords, or are specialized on vases, or statues, or watches, or fire arms,
etc. Automatic shape classification and
mark
-
up

are not applicable in this case because items in a
collection can often b
e described best in terms of their characteristic shape parameters. This
parameter set, however, is dependent of the shape class under consideration, so there has to be a
way for the human operator to define a “parametric shape template” that explains to t
he computer
how to measure the characteristic parameters. This template then allows for automatic classification
of the shapes in the collection, thereby obtaining a more targeted shape database


or merely a
digital shape library


with much richer semant
ics. The semantic shape classification is the basis for
many novel applications, including collection management, outlier detection, and even statistical
data mining.


Problem Statement

The availability of collections of real
-
world artefacts as three
-
dimensional digital assets will enable
a wide range of novel methods from cultural and art history, over collections management, to
statistical comparative studies. Easier access to collectio
ns will stimulate the exchange of research
results and facilitate a broader, more complete, and more precise historical knowledge. All this is
possible, however, only with rich high
-
level shape semantics. “Semantics is the key” describes the
difference bet
ween a simple database and a digital library: Shape is not just treated as data (e.g.,
scanned triangle meshes), but the digital library must also contain a formalized notion of the
“meaning” of the shape data that goes beyond, e.g., statistical shape desc
riptors.


In order to meet
this goal, we have identified four main problems.

The
first problem

is the cost
-
efficient 3D
-
acquisition of a collection of real
-
world shapes. 3D
-
acquisition is greatly facilitated today by photogrammetric methods. Especially i
n the Cultural
Heritage (CH) sector, these methods have enormous potential and may very well revolutionize
archaeological documentation: The intention is to go for 3D acquisition methods that are user
-
friendly and portable to be used in a very friendly way
. Having for instance image based 3D
acquisition is much easier and cheaper than operating a 3D scanner and would allow the acquisition
done by non
-
experts.


In particular; methodologies purely based on photo sequences can cover an object faster in a more
complete way, not having to rely on bulky acquisition setups; CH objects will typically exhibit a
rich texture and are typically well suited for photogrammetric methods; image registration and
sparse point cloud generation are completely automatic; and eve
n dense matching and 3D
reconstruction of individual image sequences can be largely automated. For larger and more
complete objects, however, a large number of image sequences must be processed. One problem is
scalability, since very large amounts of data
must be handled routinely; another is interactivity and
quality control, to overcome the problem of detecting too late that parts are missing; and the third is
sustainability in the sense that new additional image data can still be integrated later on. In

this
project it is believed that having “shape” knowledge on certain types of CR specimen, can also
benefit the guidance of the acquisition on the one hand, and the final creation of the model. It
should be noted that the approach will not be restricted t
o purely photographic methods, also
FP7
-
ICT
-
2011
-
9


STREP
proposal

18/01/
12

v1


SHAPEATLAS

Proposal Part B: page

4


methodologies involving structured light and or photometric based methods can and will be tuned
towards user
-
friendly acquisition.

The
second problem

is to describe the typology of a collection of similar objects, like a
mphorae or
swords. Automatic solutions exist for shape search and shape matching; this is a well
-
established
research field. These approaches, however, are not applicable in the case of collections because the
differences in shape are too subtle. Automatic

shape classification is very effective for
discriminating shape classes (amphora vs. sword). With shape collections, however, small intra
-
class shape differences can make a large difference in classification. Subtle shape differences are
just the informat
ion that art historians are typically interested in most because they allow drawing
various conclusions, e.g., about the era, the location of origin, about cultural influences, or the
diffusion of technology. There are many examples of classical typologies

including column types
(Doric, Ionic, Corinthian), helmet types (Attic, Thracian, Corinthian, Illyrian, Phrygian), and also
amphorae (about 66 types, systematic classification by Heinrich Dressel and others). So the task is
to find user friendly ways for
defining “parametric shape templates”: Given a digital artifact, an art
historian must be able to describe which parts of the shape are more important for a particular
classification than others


keeping in mind that any classification is just an interpre
tation, i.e.,
several classification schemes may exist in parallel.

The
third problem
concerns the documentation of large collections of shapes. Many museums
suffer from a considerable backlog in documentation. Museum archives are full of objects that stil
l
need to be described, but this tedious and costly. Every collection item must be described,
classified, photographed, and measured. Putting objects on display additionally requires cleaning
and restoration; but before deciding what is displayed, it is es
sential to document the collection.
Again, photogrammetric methods have a great potential: Once a 3D reconstruction from one or
more photo sequences is available, the 3D shape can be computed. Measurements could then be
taken from the digital shape, using
appropriate easy to use 3D
-
measurement tools. Once a
measurement procedure for one object is defined, also similar objects can be measured using the
same procedure. An obvious extension would be to carry out these measurements automatically for
all objects

that belong to the same shape class, thus progressing from semi
-
automatic to automatic
shape measurement.

And finally, the
fourth problem

is searching and browsing a large collection of digital artefacts.
Conventional shape search uses an input shape and

produces all similar shapes in the database as
output. This is not very reasonable for a collection, where all shapes are similar. Conventional
similarity measures are based on shape descriptors and are computed by comparing feature vectors.
This is indis
pensable for managing larger collections, as this allows fast navigation through
different shapes. An important extension, however, would be to complement it by the parametric
search based on shape measurements. So the user could use similarity
-
based searc
h to interactively
refine the search results, and at some point switch to parameter/measurement
-
based search.


Parametric modelling and the inverse problem, shape fitting

3D modelling of a collection of complex shapes can be greatly improved in efficiency

by
parametric modelling. Conventional 3D modelling systems (SketchUp, AutoCAD, 3DStudioMax,
Maya) use a forward
-
modelling style where a shape is interactively edited and changed until it
matches the specification; result of the modelling process is a set
of triangles or NURBS patches.
Parametric high
-
end modelling systems (Pro/Engineer, CATIA) internally use a parametric
description that allows for parameter changes to easily create variants of a model, e.g., when the
specifications change.

Generative mode
lling goes one step further in that a shape is described as a sequence of shape
modelling operations. This reflects a paradigm change from objects to operations, and permits for
creating libraries of parametric shapes that can be nested. Fig.
X

shows an example of a
hierarchically structured Gothic windows where (i) the defined regions (circle, fillets and sub
-
windows) can be instantiated with different shapes from a library (top left), and (ii) the hierarchy
can be further refined by inserting
sub
-
shapes that again define new regions (bottom left). This
FP7
-
ICT
-
2011
-
9


STREP
proposal

18/01/
12

v1


SHAPEATLAS

Proposal Part B: page

5


example uses the GML shape description language for generative models developed by partner
TUG
(Havemann, 2005)
.



Fig.
X
: Generative models of Gothic window tracery from
[Hav05]. The genera
tive description is
not only parametric, but also hierarchical; different sorts of sub
-
shapes can be inserted from a
library.


While this solves the forward modelling problem for collections, SHAPEATLAS is confronted with
the inverse problem, i.e., shape
analysis, and not shape modelling. One way to analyse a shape,
however, is to use an analysis
-
by
-
synthesis approach. This means to re
-
generate the scanned target
shape by finding the appropriate shape parameters for it, as is illustrated in Fig.
X
. Althoug
h this
seems to be an elegant way to solve the analysis problem, it turns out that finding the parameters for
non
-
trivial shapes numerically is extremely hard, as is explained in the next section.


Abandoning fully automatic fitting of complex parametric shapes

Fitting to a mesh a detailed parametric template model with many degrees of parametric variability
implies a high development effort and computational costs. The conventional approach is to s
earch
for optimal parameter configurations in a piecewise linear, high
-
dimensional, curved shape
parameter space. This naturally leads to non
-
convex constrained optimization problems that are
difficult to solve and can easily run into undesired local minim
a. Random sampling and multi
-
resolution approaches can help, but processing times remain prohibitive. Also unsolved is the sheer
amount of memory required.


Fig.
X
: Inverse generative modelling. Given a scanned shape (left) and a parametric shape (right)
,
the goal is to find the appropriate set of parameters that gives the optimal match to the scanned
shape.

FP7
-
ICT
-
2011
-
9


STREP
proposal

18/01/
12

v1


SHAPEATLAS

Proposal Part B: page

6



Another important problem is that CH artefacts are often delicate and elaborate pieces of
craftsmanship that simply defy a notion of the industrial

construction process for which generative
modelling approaches work so well. Virtually all objects produced in the pre
-
industrial era have
shape irregularities and imperfections that are difficult to capture parametrically. Automatic fitting
is almost imp
ossible if corresponding parameters are not incorporated in the template model
beforehand. Potentially, though, methods from statistical shape analysis could help to determine the
variability of a shape class in order to assist the operator in producing a
suitable parametric model.


Towards shape semantics from shape measurements

SHAPEATLAS will therefore pursue a different approach that is conceptually novel, easily
extensible and, most importantly, directly useful for CH experts. The idea is to develop a
fast and
lightweight interactive shape measurement tool. It proceeds by user
-
assisted fitting to a given digital
CH artefact a structured shape detector that is composed of a hierarchy of shape detector primitives.
The result is semantic information, namel
y a rich set of object
-
adapted measurements. This
addresses a practical problem in CH, the high cost of measuring and characterizing artefacts: The
2009 inventory catalogue of the Museum of Archaeology in Castle Eggenberg, Graz, lists for a
family of Slove
nian military helmets from 4
th

to 1
st

century BC the following parameters: height,
diameter of inner and outer rim, and material thickness.


The complexity of measuring such values
is one reason for the large backlog of unprocessed artefacts in museum arc
hives. Humans are good
at determining and distinguishing shape categories, while computers are well
-
equipped for precise
and fast numerical fitting. Fitting works extremely fast if it starts close to an optimal parameter set
with respect to alignment and s
hape parameters.

A
shape map

is a user
-
defined model for 3D shape measurement and classification. Technically, it
is a hierarchically structured complex shape detector that consists of simpler shape detectors, down
to primitive detectors. The goal is to pr
ovide a library of primitive shape detectors and a tool to
arrange them hierarchically. The main advantage of this approach is that shape features are
disambiguated: Small features can make large semantic differences, e.g., decorative tin plates may
easily

be mistaken for shields. Subtle shape features are overlooked by unsupervised fitting
approaches, or ignored because no parameters exist for them in the model. Hence, the user supports
the system by making explicit which shape feature is important and whi
ch is not. This leads to a
two
-
stage process:

1.

Definition

of a shape map:

The user starts by loading a triangle mesh of a prototype object,
e.g., a helmet. As first primitive detector he selects an empty
-
sphere detector for the head
space and roughly aligns

it. The fitting procedure binds its four float parameters (origin and
radius). Next, a below
-
plane is selected, roughly aligned, and fitted to the base of the
helmet; then two cylinder detectors yield the radii of inner and outer rim. To fix the pose,
the

rotation around the central axis is computed by applying either a symmetry
-
plane or a
SVD decomposition of the mesh vertices. Finally, the height is measured by inserting
another fitting plane from above, which is constrained to be parallel to the below
-
p
lane.


Once such a hierarchical detector is defined
, it

is stored in a shape map library. As by
-
product this leads to
a
shape

taxonomy

as shape maps can later be successively refined and
specialized to match sub
-
classes of shapes in the collection.


2.

Instantiating a shape map
:

The user starts by loading the triangle mesh of a newly acquired
helmet. He selects from the shape map library the helmet map and roughly aligns it. The
fitting process starts immediately, successively “snapping” more and more sh
ape detectors
to the mesh, and it stops whenever further user assistance is required. Since this is the costly
part, the user
-
assisted fitting process must be very fast. We target a time budget of three to
five minutes max. This will require finding approp
riate “snapping strategies”.


FP7
-
ICT
-
2011
-
9


STREP
proposal

18/01/
12

v1


SHAPEATLAS

Proposal Part B: page

7


List of primitive shape detectors

Shape maps are combined from a well
-
set of primitive shape detectors. Thus, they constitute the
basis of the method. These primitives must be designed in such a way that they are both
meaningful
and easily applicable. Therefore they are not “numerical magic”, but they have an intuitive meaning
and behave in a robust and predictable way. The shape detectors determine the “semantic
vocabulary” for describing and analysing shape, and must
therefore be expressive enough to allow
“formulating” what constitutes the shapes of a collection. The set should therefore contain at least:




Min
-
max probes:
planar rectangle or disc with a linear spring. It is positioned in space and
moves in normal dire
ction in a definable range until it is blocked by the target mesh. So the
detector might "press" from the outside inwards or from inside outwards.



Segmentation volume (box/sphere/cylinder):

To force fitting algorithms to use only selected
portions of the
target mesh. The shape is segmented into regions to apply there other
detectors.



Fitting volume (box/sphere/cylinder):

Classical fitting of a shape primitive using the one
-
sided Hausdorff distance as fitness measure in a RANSAC approach inside a segmentati
on
volume.



Crease, Edge and Corner detectors:
These are shape features that often carry important
semantic information. They can also be configured to search for rectangular configurations
(right angles), and for convex or concave cases.



Slippage Analysi
s (profile swept along line or circle segment):
When applied to a surface
point, this detector looks for a high
-
curvature profile in a normal plane that can be swept
along a low
-
curvature line or circular arc. The result is both the profile curve and the s
pine
of the sweep. As the spine length is maximized this can, e.g., yield the height of a column.



Orientation plane:
planar rectangle or disc that supports the notion of principal planarity. A
coarse brick wall for example might be roughly planar but noisy
, have fissures, holes, etc. A
weighted least squares fit incorporating local first
-
order information can compute a
preferred normal direction. Alternative methods are ICP or RANSAC plane fitting.



Symmetry plane:

A plane is roughly aligned inside a fitting

volume, the four plane
parameters are optimized using a RANSAC approach followed by mirroring and
approximate Hausdorff distance.



Principal axes:
The least squares normal equations can be solved by singular value
decomposition (SVD). This yields a coordin
ate system for initial alignment of subsequent
detectors.



Similarity search region:
This establishes the link to the conventional shape
-
based search.
The user can specify a region of the surface to be indexed using a statistical shape similarity
measure. T
his way also shapes that are too complex to describe explicitly can be
distinguished (e.g., coins showing a face or a symbol, floral vs. geometric ornaments, etc.).


Using the shape map: The hierarchical snapping process

Figure
X

shows mock
-
up examples, e.g., the decomposition of a column acquired from the Herz
-
Jesu church (Graz) into three basic cylindrical shapes, a capital (symmetric) and a box
-
shape on top.
The shape map is applied (step 2), e.g., by first clicking on the main
cylinder, which triggers the
“snapping” of the first shape detector. The other two (collinear) cylinders are searched for in its
vicinity, and once they also snap in, the cylinders are elongated until the column capital is reached.
The next shape detectors

are the symmetry and box detectors, which are supposed to look in a
certain search distance for mesh parts to snap to.


FP7
-
ICT
-
2011
-
9


STREP
proposal

18/01/
12

v1


SHAPEATLAS

Proposal Part B: page

8




Figure
X
: Shape map mock
-
ups. Left: A capital detector might consist of a fitting box (top), a
symmetry plane (middle) in a
segmentation box, and three fitting cylinders (bottom). Right: after
a rough initial alignment, the cylinders extend their height until the slippage condition is violated.
Planar regions can be found in many ways including slippage analysis, RANSAC plane f
itting,
SVD, ICP.


This example illustrates that the snapping order is important. A good shape map can be applied by
using only a few mouse clicks to “anchor” some strategically chosen places of the model, thereby
spanning a reference frame for other dete
ctors revealing more of the detail structure of the model.
The information flow is not only top
-
down, however, since information from later stages in the
snapping process can also be relevant for improving the accuracy of detectors that snapped in
earlier.

It will also be necessary to realize a backtracking strategy to deal with the situation that
required detail cannot be found. Shape maps will internally also be represented as GML code. Note
that in this case, GML is used not for describing parametric sha
pes, but shape maps, i.e.,
hierarchically structured parametric shape detectors. In either case, however, the expressiveness of a
full programming language is required.


Shape Search

As the amount of 3D data becomes more common, and also outside the field
of CR is rapidly
expanding (e.g. through portals such as Google Warehouse) the need for effective data
-
mining
(
Funkhouser, 2004; Toldo, 2009; Ovsjanikov, 2011; Knopp, 2010
) is rising too. In this project, we
will therefore also consider the task of improvi
ng shape retrieval with the help with the help of user
guided, and fully
-
automatic, unsupervised, generic and fast verification and expansion.

This work draws heavily from a variety of existing research in the related areas of content based 3D
retrieval and image
-
based search. Ideally the numerical description of shapes and the comparison
metric would be optimized for perfect retrieval. However,
it is difficult to quantify semantic
concepts or to find the appropriate mathematical representation. Therefore, despite the large amount
of work done on finding generic detectors and descriptors suited for visual tasks
(Lowe, 2004;
Willems, 2008)
, signifi
cant effort was also invested in devising better features
(Akbar, 2007;
Papadakis, 2008; Philbin, 2010)
, attributes
(Philbin, 2008; Ferrari, 2007)
, distances
(Elad, 2002;
Akbar, 2007; JSegou, 2007)

and projections. A major drawback of these approaches is t
he
requirement of user supervision, inability to generalize (in case of typical user
-
driven approaches)
and delay from incorporating relevance feedback into online learning. Generic off
-
the
-
shelf 3D
features
(Willems, 2008)

in BoW (bag of words) based meth
ods
(Toldo, 2009; Ovsjanikov, 2011;
Knopp, 2010)

have been shown to be robust to noise, deformation, orientation etc. and we exploit
these in our work for the retrieval task. Interestingly, though BoW approaches have been used in
shape search, relevance fe
edback has mostly relied on vanilla features
(Elad, 2002; Papadakis,
2008; Akbar, 2007)
.

Computational time and accuracy often have to be played off against each other, when examining
the semantic relevance of search. Regardless of the chosen representati
on, most methods end up
improving search relevance in a somewhat cascaded or iterative manner. For example, two (or
more) representations (feature or distance wise) for an object may be constructed: one in which
FP7
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ICT
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2011
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12

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SHAPEATLAS

Proposal Part B: page

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search can be performed fast and another in
which accuracy can be improved. Improvement of
accuracy and performance is still to be achieved in a variety of ways:




incorporating user feedback (most popular in 3D
(Minka, 1996; Elad, 2002, Papadakis,
2008; Hoi, 2011
),



structural consistency (as in 2D

(Philbin, 2008, Mikulik, 2010; Chum, 2011
)



a variety of heuristics e.g. pseudo
-
relevance feedback, multiple queries etc. (see
(Lou, 2003,
Bang, 2002; Papadakis, 2008
).


Application use case: Plaster moulds for ornaments of the Regency period in Brighton

A paradigmatic example for the necessity of managing collections of similar shapes


in that case
even self
-
similar shapes


is the excellent collection of historical plaster moulds hosted by partner
BTCT.

NICK/PHIL: Please describe use case





Fig.

X: Wooden moulds for plaster ornaments of the Regency period in Brighton (19
th

century).
There are over 700 of these original moulds, which are still in use today. However, subtle shape
differences make it quite difficult to find the right mould when carr
ying out reconstruction work.




Progress beyond the state
-
of
-
the
-
art

1.2

Shape Search

In this project several improvements user feedback, structural consistency, a variety of heuristics
e.g. pseudo
-
relevance feedback, multiple queries etc. The goal is
to create a system that is able to
learn shapes unsupervised. Whenever certain feature correspondences have been found between
objects; an iterative scheme would try to combine different instances that corroborate one another.
Finally the object definition

is not described anymore by its individual instances, but by its
expanded version, or simply stated its average (although that does not cover the real context)

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In the quest for such a scheme, a 2
-
pass approach is planned based on query verification and
expansion. First, the results of a vanilla search scheme based on vanilla BoW based retrieval are
weakly verified according to the spatial configuration of the query object. This is followed by a
second pass which retrieves shapes similar to the verified s
et of results to create an expanded list.
While the original vanilla BoW search is fast, giving the method many possible candidates, the
more expensive verification and expansion searches in an alternative space, lead to an increase in
accuracy of results.

The query expansion was found as an optimal retrieval method when searching
is defined as a classification task
(Efron, 2008)
.


BoW representations succeed in representing a shape in terms of feature occurrence and being
orientation and noise invariant bu
t fail to capture more layout information. An improvement over
vanilla search, was the analysis of word co
-
occurrences
(Sivic, 2004; JSegou, 2009;

JSegou, to
appear)

and more recently, structural constraints
(Chum, 2011; Arandjelovic, 2011)
. Actual shape
h
as more complex representation: e.g. statistical models, articulated models (among other graphs),
templates etc. Fitting such models to a given shape can involve working our parameterisation and
correspondences. An exhaustive and comprehensive fitting proc
edure can be expensive, rendering
this useless for the purpose of shape verification in search. Hence, we extend schemes that leverage
feature co
-
occurrence to take into account their mutual spatial layout. Though a very loose
approximation of more complic
ated shape models, this scheme of weak structural verification
proves very effective

in improving search performance.


In the above scheme it is more or less assumed that the objects are defined by their overall shape.
This is in general also the main assu
mption in literature. However, shapes can be related to one
another as well because of correspondences that are found locally; i.e. on parts of the object. For
instance, a collection of statues or reliefs may correspond because they all contain faces; howe
ver,
the context in which they are presented may be completely different.



Therefore additional methodologies will be created to indicate areas of interest, which can then be
used to fine
-
tune the
properties of these subshape, i.
e
.

descriptors. The query software will enable to
look and track down objects in the database that show such partial features.


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S/T methodology and associated work
plan

1.3
1.3.1

Work package list



Work
package

No

Work package title

Type of
activity
1

Lead

partic

no.
2

Lead
partic.
short
name

Person
-
months
3

Start

month
4

End

month
4

1

Management

MGT

1

FhA

36

1

36

2

Acquisition

RTD

4

KUL

45



3

Shape
Map Definition

RTD

3

TUG

33



4

Shape Indexing

RTD

4

KUL

45



5

Shape Query

RTD

3

TUG

33



6

Integration and Evaluation

RTD

2

UOB

27



7

Dissemination

DEM

1

FhA

6




TOTAL




225




1.3.2

List of d
eliverables


Del. no.

5

Deliverable name

WP no.

Nature
6

Dissemi
-
nation

level

7

Delivery
date
8

(proj.

month)






















1


Please indicate
one activity (main or only activity) per

work package:

RTD = Research and technological development
; DEM =

Demonstration; MGT = Management of the
consortium

2


Number of the participant leading the work in this work
package.

3


The total number of person
-
months allocated to each work package.

4


Measured in months from the project start date (month 1).

5


Deliverable numbers in order of delivery dates. Please use the numbering convention <WP number>.<number
of
deliverable within that WP>. For example, deliverable 4.2 would be the second deliverable from work package 4.

6


Please indicate the nature of the deliverable using one of the following codes:


R

= Report,
P

= Prototype,
D

= Demonstrator,
O

= Other

7


Please indicate the dissemination level using one of the following codes:


PU

= Public


PP

= Restricted to other programme participants (including the Commission Services).


RE

= Restricted to a group specified by the consortium (including the Commission S
ervices).


CO

= Confidential, only for members of the consortium (including the Commission Services).


8


Measured in months from the project start date (month 1).

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1.3.3

List of
m
ilestones



Milestone
number

Milestone
name

Work package(s)
involved

Expected date
9

Means of
verification
10






























9

Measured in months from the project start date (month 1).

10

Show how you will confirm that

the milestone has been attained. Refer to indicators if appropriate. For exa
mple: a
laboratory prototype
co
mpleted and running flawlessly;
software released and validated by
a
user group; field survey
complete and data quality validated.

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1.3.4

Work package
description



Work package number

1

Start date or starting event:

Month 1

Work package title

Management

Activity type
11

MGT

Participant number

1

2

3

4

5



Participant short name

FhA

UOB

TUG

KUL

BTCT



Person
-
months per
participant

18

18







Objectives


WP1 covers all activities related to project management

and reporting to the commission
. Its p
rimary goal is
to ensure that research work is focused and carried out according to the overall project vision and work plan,
within the time and budget constraints and with efficient use of available resources.


Description of work

(possibly broken down

into tasks) and role of partners

The overall approach to management is described in Section 2.1 and implemented via the specific
tasks

described below. WP1 will entail day
-
to
-
day central management activities, including establishment and
maintenance of to
ols for intra
-
project communication and collaboration, development and maintenance of
the project website and its content management, communication and collaboration infrastructure,
organization of project meetings, and the development and delivery of proj
ect reporting, both internally to
the project and externally to the Commission.
Furthermore
, WP1 will undertake the preparation and
implementation of quality plans, establish and execute respective monitoring processes, and
establish and
implement
risk man
agement and contingency plans.

The
project management and coordination will be performed by Fraunhofer Austria
(with focus on technical
coordination)
and University of Brighton

(with focus on administrative management):

Task 1.1. Quality assurance & risk m
anagement

(FhA, UBO)

This Task will formulate and outline the procedures for quality assurance and risk management to be
followed and maintained for the management of the project. The output of this Task is a Quality Assurance
& Risk Management Plan (QA&RM
).

This Task also covers
a

continuous assessment of project uncertainties
and challenges and proactive elaboration and implementation of contingency measures, as well as corrective
actions if and where needed. This plan will incorporate, elaborate on and e
xtend potential risks as identified
at the proposal stage.

Task 1.2. Knowledge management and IPR resolution (
FhA, UBO
)

The consortium’s approach to intellectual property rights (IPR) issues and the management of knowledge
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establish and maintain mailing lists and an on
-
line real
-
time communication platform (e.g. skype

or
other teleconferencing tool);



establish and maintain the internal project web
-
based collaboration platform and document



11

Please indica
te
one activity (main or only activity) per

work package:


RTD = Research and technological development
; DEM = Demonstration
; MGT = Management of th
e consortium
.

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repository;



maintain procedures for monitoring internal communication and collaboration (as will be detailed in
the QA&RM Plan);



ens
ure and monitor smooth communication within the consortium, with EC and with the External
Advisory Board;



organize and manage project meetings (including detailed agenda and minutes for each meeting).


Task 1.4. Administrative & financial management (
UBO
)

This task will initiate, set and implement the consortium contract, including the consortium agreement, the
overall administrative management processes and routines. This task will also take care of all the financial
aspects and routines related to the pro
ject. In particular specific actions are:



establish and monitor project internal reporting schedules and procedures regarding administrative
and financial issues, including 3
-
month internal progress reports;



prepare the necessary progress reports and finan
cial rec
ords according to EC guidelines
;



support to partners in completing their contributions to the Periodic Reports which include the Cost
Claims and any required Certificate on the Financial Statements

(CFS)



obtain Audit Certificates (C
FS
) from partners (where applicable).



Deliverables

(brief description) and month of delivery

M
.1.1.

Quality Assurance & Risk Management Plan
(Month 1)

The QA&
RM plan will define in detail all procedures for quality assurance in project communication,
collaboration and deliverables.


M
.1.2.

IPR management plan
(Month 1)

The initial version of IPR management plan (M01) will detail the plan and specific procedure
s needed to
implement the Consortium Agreement (Annex to the Grant Agreement) with respect to knowledge
management. Subsequent versions (M13, M25) will include detailed descriptions of project foreground
knowledge and (if needed) amendments to the Consorti
um Agreement.

M
.1.3.

Project collaboration & communication infrastructure

(Month 1)

This deliverable entails the web
-
based collaboration & communication platform that will be used during the
project. Content and activities will be developed during the cour
se of the project. An accompanying short
report will present a basic outline of the infrastructure and its use.

M
.1.4.

Project Progress and
f
inancial
r
eports

(Month 1)

Regular progress and financial reports as mandated by the Grant Agreement and the EC. T
hese include
yearly and final project report, or any other report that may be requested by EC.

M
.1.5.

Updates to D.1.1.


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M
.1.6.

Updates to D.1.1.


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7
.
Upda
tes to D.1.1.


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D.1.8
.

Final Report

(Month 3
8
)

The final, financial

report documents the project’s scientific, economic and social success.




Work package number

2

Start date or starting event:


Work package title

Acquisition

Activity type

RTD

Participant number

1

2

3

4

5



Participant short name

FhA

UOB

TUG

KUL

BTCT



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Person
-
months per
participant


12


27

6




Objectives


The Brunswick Town Charitable Trust

defines the use
-
case scenario, in which they provide 3D objects.
These objects are photographed by U
O
B and processed by KUL. The reconstruction process will need some
adjustments and new features (multi sequence processing, etc.), which will be added and implemented within
this work package.


Description of work

(possibly broken down into tasks) and r
ole of partners

Task 2.1 Improvement of photogrammetry acquisition technique (KUL)

KUL will provide the necessary tools for 3D
acquisition
.



The acquisition is chosen to be image based, in order to provide a flexible non
-
expert approach for
the 3D
digitization. Proper procedures will be defined to create image sequences, which will be
processed by a cloud based reconstruction service called Arc3D.



An assessment will be carried out on additional complementary techniques that involve additional
struct
ured light and photometric stereo, in order to verify whether they enhance the acquisition
pipeline for the given dataset or business case.



Since the acquisition pipeline is fully self
-
contained and is not dependent on third parties, the
scanning can be ac
companied by immediate shape analysis. Therefore the immediate output is only
restricted to the 3D model, but also shape related characteristics will be integrated and can be fed
back into the system.


Task 2.2 Define and develop user case scenarios (U
O
B,
BTCT)

The experiments will be designed and conducted by groups other than those responsible for the development
of the acquisition tools. The intention is to acquire enough 3D content using the improved photogrammetry
tool Arc3D in order to test the shape
map core technology. The result of each experiment will be documented
in a 3D repository along with the metadata regarding its capture. It is expected that the data for this scenarios
will include ornaments of heritage buildings as well as architectural el
ements.


Task 2.3 Documentation of domain knowledge on shape classification (U
O
B, BTCT)

This task will develop the documentation on the domain knowledge of the user case scenarios, in particular
regarding how shape is defined according to the experts in t
he area. This knowledge will be documented and
will serve as input for user requirements to support the development work packages (WP3, WP4, WP5,
WP6).



Deliverables

(brief description) and month of delivery

D.2.1.

User case scenarios definition and protocols for implementation (M
onth
6)

D.2.2.

Domain knowledge and user requirements based on user case scenarios (M
onth
9)

D.2.3.

Alpha version of photogrammetry techniques (M
onth
12)

D.2.4.

Document acquired content (M
onth
24)

D.2.5.

Beta version of photogrammetry techniques (M
onth
24)




Work package number

3

Start date or starting event:


Work package title

Shape Map Definition

Activity type

RTD

Participant number

1

2

3

4

5



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Participant short name

FhA

UOB

TUG

KUL

BTCT



Person
-
months per
participant


12

27






Objectives


TU Graz realizes the concept of local shape maps; i.e. a user
-
defined regions of interest, which have special
features. These regions and features will have a special weighting in the
feature
-
based clustering within the
shape indexing (see WP 4). To describe a shape template (consisting of these shape maps) these local shape
maps have to be combined using a graph structure (called atlas). U
O
B will define these graph structures.


Description of work

(possibly broken down into tasks) and role of partners

A shape map is a user
-
defined model for 3D shape measurement and classification. It consists of elementary
parametric shape detectors that are combined in a hierarchical fashion to
obtain a user
-
defined pattern
matching template for a complex shape. The objectives of this work package are to develop the shape map
core technology, and to create an easy
-
to
-
use interactive 3D software application for the creation of shape
maps. This app
lication shall enable users with a non
-
technical background (e.g., from the Cultural Heritage
sector) to explicitly describe, for the shapes of a collection, which particular properties of a shape constitute
its class membership, which shape parameters are

to be measured, and where on the shape they are
measured.

In particular, the tasks in WP 3 are:



Primitive shape detectors (see Section 1.1): Min/max probes, segmentation volumes
(box/sphere/cylinder), fitting volumes (box/sphere/cylinder), crease/edge/cor
ner detector, slippage
analysis, orientation plane, symmetry plane, principal axis, similarity search region.



Shape map hierarchy (see Section 1.1): The multi
-
level snapping proceeds along the hierarchy where
sub
-
detectors that are defined in the reference

frame of their parent detector. Possible positions and
orientations of the child can be prescribed.



Shape map software: It offers a set of tools to create, place, and configure primitive detectors, and to
refine the hierarchy by specifying search constra
ints for sub
-
detectors. This tool is used both for
defining (once) and for instantiating (many times) a shape map.

In the tools the emphasis is on the efficient computations that allow interactive speed, which is for some
detectors are very challenging ta
sk.



Deliverables

(brief description) and month of delivery










Work package number

4

Start date or starting event:


Work package title

Shape Indexing

Activity type

RTD

Participant number

1

2

3

4

5



Participant short name

FhA

UOB

TUG

KUL

BTCT



Person
-
months per
participant

18



27





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Objectives


feature vector
-
based clustering and local feature detection (KUL), graph matching for shape maps (FhA)


Description of work

(possibly broken down into tasks) and role of partners

Basic shape
descriptors will be generated based on 3D surf features. Through a
n

iterative scheme of
verification and expansion; different types of objects can be characterized and distinguished.

Proper distance measures will be put in place to provide the necessary co
rrespondence.



Deliverables

(brief description) and month of delivery










Work package number

5

Start date or starting event:


Work package title

Shape Query

Activity type

RTD

Participant number

1

2

3

4

5



Participant short name

FhA

UOB

TUG

KUL

BTCT



Person
-
months per
participant


6

2
1






Objectives


Interactive online application for searching and browsing the shape database showing a dynamic live result
set, combining shape
-
based search and parametric search. Parameter selection either form
-
based or by
automatic parameter clustering through relevance

feedback. Refined shape maps are offered for searching
sub
-
classes in order to restrict and specialize the scope of the search.


Description of work

(possibly broken down into tasks) and role of partners

This WP is concerned with the end
-
user frontend fo
r querying the shape database. The challenge is to
combine shape
-
based search with parameter
-
based search in a seamless way. The interface will support
query by example as well as form
-
based search, providing the shape parameter set of the chosen shape cla
ss
in a refineable way to allow also searching for more specialized sub
-
classes (from hat to helmet); but also
generalization is possible, going from a specific shape to the super class (from helmet to hat).

The specific tasks are:



Relevance feedback: The

user can select the most relevant of the currently offered search results.
The result set is modified according to (i) shape similarity and (ii) parameter similarity, using
automatic parameter clustering to determine the most probable search direction.



Sh
ape map editing: Every object shown as search result has one or more associated shape maps. The
user can select one of them and also alter the instance parameters, and the result set is updated
accordingly (select thin amphora, increase girth parameter, re
sult set: thick amphorae)



Parameter inspection: The user can select any search result and look at the instance parameters of the
shape map; select one parameter; and re
-
order the search result with respect to this parameter.



Specialization/generalization:

Since the shapes in a result set are similar, they have similar
specializations and generalizations. The respective shape maps are shown to direct the search.

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Representative instance
SVEN

As in WP3, interactivity is the key for maintaining a fluent and ta
rgeted search. This will require developing
optimization strategies for computational efficiency.



Deliverables

(brief description) and month of delivery

D.5.1.









Work package number

6

Start date or starting event:


Work package title

Integration and Evaluation

Activity type

RTD

Participant number

1

2

3

4

5



Participant short name

FhA

UOB

TUG

KUL

BTCT



Person
-
months per
participant


1
2

6


9




Objectives


This work package will create an integrated platform with the shape map
core technology, the shape
indexing and query mechanisms, which allow a user to explore a collection. Furthermore, it will undertake
an evaluation of the tools developed as effective solutions to the problems addressed by the project.


Description of work

(possibly broken down into tasks) and role of partners

Task 6.1 Integrated platform for search and visualisation (
UOB
)

This task will develop an integrated platform integrating the different technologies developed within the
work packages into a tool whi
ch can be deployed in the cultural heritage sector.

Task 6.2 Evaluation of technology (U
O
B,
BTCT
)

This task will test different aspects of the tools for 3D artefacts in controlled experiments designed to
evaluate both individual tools and the working
practices their use implies. The results will be a systematic
evaluation used to inform improvements to the tools and to understand the requirements for deployment in
the field. These experiments will be developed using different case scenarios provided by

the heritage
organisations.



Deliverables

(brief description) and month of delivery

D
.
6.1.

Alpha version of integrated platform (M
onth
24)

D
.
6.2.

Testing protocols (M
onth
30)

D
.
6.3.

Feedback from testing (M
onth
36)







Work package number

7

Start
date or starting event:


Work package title

Dissemination

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Activity type

DEM

Participant number

1

2

3

4

5



Participant short name

FhA

UOB

TUG

KUL

BTCT



Person
-
months per
participant

3

15



3




Objectives




Disseminate the project activities and
results supported by different mechanisms, including publications
and events.



Create and moderate a website supported by the project partners.



Provide quality publications for the EU digital cultural heritage community.



Explore the potential for exploitat
ion.


Description of work

(possibly broken down into tasks) and role of partners

Task 7.1 Website (U
O
B, FhA)

The website aims to support those working in digital cultural heritage by providing several avenues for
professionals to connect and access cutting edge information, tools produced by the project and events. The
website will be updated constantly by the pr
oject partners and will have different levels of security and
access depending on user status.


Task 7.2 Publishing (UOB, FhA)

The project will provide a biannual newsletter that will serve to inform, engage and stimulate professionals
in digital cultural

heritage. The newsletters will focus on summaries of current project news, issues, research
and events.


Task 7.3 Dissemination and demonstration events (U
O
B, FhA)

Biannual events will be organised at existing and ongoing conferences such as CAA, VAST, V
SMM. These
events aim to:



Connect with relevant professionals,



Demonstrate and disseminate the project results,



Encourage those working in CH to deploy the technologies developed by the project

This will also include the production of dissemination mater
ial to support these events, such as the
production of printed material (e.g. publications, leaflets), video, and participatory fees.


Task 7.4 Exploitation (U
O
B, BTCT)

This task will map out the potential market and opportunities for exploitation of the

project outputs. It will
adopt a layered approach, considering the specific opportunities associated with the use
-
case scenario
dataset, the broader potential for application to a range of reconstructive CH scenarios, and the general
exploitability of the

tools and techniques developed, individually or in combination. It will use the
dissemination activities and evaluation studies to engage with potential clients, identify exploitation
scenarios, map out strategies and assess risks. It will produce an Expl
oitation report summarising its findings
and making recommendations for sustainable exploitation after the project has ended.



Deliverables

(brief description) and month of delivery

D.7.1.

Website
(
Month 1, 30
)

D.7.2.

Report
of website (
Month 7, 14, 21,
29)

D.7.3.

Results on networking activities
(
Month 12, 24)

D.7.4.

Exploitation report
(
Month 30
)

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1.3.5

Summary of
effort



Partic.
no.

Partic.
short
name

WP1

WP2

WP3

WP4

WP5

WP
6

WP7

Total
person
months

1

FhA

18



18



3

39

2

UOB

18

12

12


6

1
2

15

75

3

TUG



27


2
1

6


54

4

KUL


27


27




54

5

BTCT


6




9

3

18

Total


36

45

3
9

45

27

27

21

2
40






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2

Implementation



Management structure and
procedures

2.1
The project management will be planned to guarantee delivery of the results on time and to
manage project
complexity. General project organization covers both technical and administrative issues.

The consortium management of SHAPEATLAS intends to reach the following objectives:



To manage communication among consortium partners.



To manage techn
ical, financial, legal and administrative activity of the consortium.



To assure that the end product conforms to the planned requirements and product description.



To preserve efficient communication with the European Commission.



To satisfy compliance wi
th EC standards and procedures for project management.

The project management plan includes the following activities:

The administrative and technical
management will carry out the project management. It will control decisions about communication among
co
nsortium members and project implementation. The implementation will also comprehend coordination
and support for reports and financial management. The coordination shall also be used for efficient
exploitation of the project outcomes.

2.1.1

Management r
oles

The

project management consists of a technical project manager and an administrative project
manager. While the administrative project manager manages project resources, advertises the
project and handles the project dissemination, the technical project manag
er supervises consortium
performance, ensures the project result’s quality and represents the project and consortium


especially the technical project manager shall communicate with external organizations.

Work Package leaders are responsible for reporti
ng and follow up of deliverables and milestones of
each particular work package. They also efficiently coordinate tasks in particular work packages.
Furthermore, they will initiate and participate to the scientific/ technical meetings necessary for
work p
rogress and report minutes. Each WP leader is responsible of ensuring the accomplishment
of the scientific and technical objectives of the WP, by assessing the quality of the outputs of the
performed work and solving local conflicts involving the tasks exe
cution. In case of failures or
major issues that affect the completion of the work foreseen, the WP leader must refer to the
technical project manager.

2.1.2

Decision
-
making bodies and m
echanisms

The project board is the advanced representation and management bo
dy of the project, led by both
project managers (administrative and technical). It will be responsible for:



Resolving and monitoring project's technical progress, taking care among work packages,
implementation and directs the execution of the Implementat
ion plan, and preparation the
reports.



All formal decisions, strategic guidance, communications with European Commission, plans
for promotion of project outcomes, etc.



Applying procedures for managing intellectual property rights and innovative outcomes o
f
the project.



The implementation of a contingency plan based on the assessment of indicators and
identified risks will be guaranteed by the project board members.

The project board will meet at least once every 4 months. Each consortium member will hav
e one
representative in the project board.

2.1.3

Reporting m
echanisms

Each consortium partner will create a management report every three months about the progress of
their work. These reports will be delivered to the technical project manager, who will combin
e
them into a unified report that will be delivered to the European Commission twice per year. The
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technical project manager will revise the reports to guarantee the completeness and consistency.
The reports contain all activities accomplished by the conso
rtium members in the corresponding
period: research and development activities, deliverables, meetings, etc.

2.1.4

Meetings, conferencing and communication t
ools

The consortium members will have regular meetings and discussions using various forms of
modern com
munication



especially Voice
-
over
-
IP techniques (Skype), teleconference systems, etc.
as a regular alternative to physical meetings in order to optimise the use of budget (reduce travel
costs and travelling time) and to reduce carbon footprint
. The
project will be handled wisely to
avoid high travelling cost and will be aimed to achieve maximum information exchange between
the consortium members. The general consortium meeting will take place periodically. If necessary,
intermediate meeting among sma
ller teams will be held. The collaboration between the consortium
members will be arranged to keep track of the project activity, obtain knowledge and deliverables.
The consortium members will have opportunity to transfer, publish, share information and co
mment
the common agenda.

2.1.5

Knowledge
management and i
ntellectual
p
roperty
r
ights

The intellectual property rights will be managed with appropriate procedures according to the
consortium agreement. The existing intellectual property rights and their knowledge

will be
determined and guaranteed with agreed procedures before the start of the project. Any intellectual
right originated in the project will be determined and regulated in the consortium agreement that
will be prepared and considered by all consortium
members.

Special activity will be taken to identify background knowledge and technologies of the consortium
partners before the start of the project.

2.1.6

Quality a
ssurance
and c
ontrol

The technical project manager will be responsible for the quality assurance
. The
quality assurance

procedures will guarantee that the highest technical and scientific qualitative of deliverables and the
requirements defined in the product description in the planning process will be ensured. The
quantitative and qualitative indica
tors will be applied to evaluate the project performance towards
meeting its objectives. Each work package leader will be responsible to check periodically whether
the deliverables meet the quality standards. Any comments and suggestions from the consortiu
m
partners how to improve the quality of deliverables will be considered. Similarly, the administrative
project manager monitors and measures costs and schedule performance.

Before submitting any document to the European Commission, a peer review process
within the
project board members will be performed.

2.1.7

Conflict m
anagement

The project consortium will consist of diverse members. Because potential conflicts are possible,
some basic guidelines for its management will be prepared and lead by the project man
agement.
Conflict management will manage conflicts between:



Individual members goals and project goals as well as



the potential disagreement according to the project's schedules and priorities.

The following plan will be prepared:


Establish Responsibiliti
es.

The technical and administrative project managers will establish
responsibility by taking charge of resolving and managing the conflict. All consortium members
have the responsibility to report any determined issues before they become conflicts.


Estab
lish Conflict Management Strategy.

The technical and administrative project managers follow
a strategy to:



detect and solve issues before they become severe conflicts,



establish a trusted environment for the consortium members to exchange ideas, and

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engage

the consortium members to express their minds.


If this strategy fails, the project board has to solve a conflict by majority vote.




I
ndividual
participants

2.2
2.2.1

Fraunhofer Austria Research GmbH

Organization profile

Fraunhofer Austria (FhA) is a fully

owned subsidiary of the German Fraunhofer

Gesellschaft. Its
research areas include Visual Computing & Digital Libraries, located in Graz and Production &
Logistics in Vienna. The business area in Graz belongs to an excellence cluster on computer
graphics a
nd computer vision with connections to Technische Universität Graz (TUG). Research
undertaken by Fraunhofer is directly aimed at promoting industrial performance, which
distinguishes Fraunhofer from other large research institutions involved in pure or bas
ic research.
This focus applies equally to contract research for industry or government, as well as to advanced
strategic research.


Key competences and role in the project


The main focus of Fraunhofer Austria
at Graz
includes three research aspects: digital society, visual
decision support and virtual engineering. In the context of SHAPEATLAS these topics are of
special interest. Fraunhofer Austria develops technologies in order to capture and expand
knowledge. Fraunho
fer Austria Visual Computing, as well as the Institute of Computer Graphics
and Knowledge Visualization of TUG are led by Prof. Dr. techn. Dieter W. Fellner. Both research
groups have 20 researchers in total and share a common infrastructure. They have gai
ned expertise
on semantic modeling, immersive visualization, and physics

based simulation in various national
and international projects.

Fraunhofer Austria will coordinate the project and manage its technical aspects. Having experience
in shape descriptio
n and semantic enrichment, FhA will also contribute to WP4 “Shape Indexing”.
Furthermore, FhA will use its networking activities to promote SHAPEATLAS and its results.


CVs of key personnel

Prof. Dr. techn. Dieter W. Fellner

is head of the Institute of Co
mputer Graphics and Knowledge
Visualization at TUG and head of Fraunhofer Austria. He is also head of Fraunhofer Institut f.
graphische Datenverarbeitung (FhG

IGD), the world’s leading institute for applied visual
computing, with applications in medicine,
automotive industries, urban management (3D

GIS),
virtual and augmented reality, and many more. His main research areas are computer graphics and
digital libraries. As principal investigator he has led a strategic initiative on Digital Libraries since
fall

1997, funded by the German Research Foundation (DFG) over a period of seven years. He has
written a widely used book on Computer Graphics (1988, 2nd ed. in 1992) and co

authored a book
with A. Endres on Digital Libraries (2000). He has regularly served on

editorial boards of leading
journals in computer graphics and digital libraries.

Dr. rer. nat. Eva Eggeling

holds a PhD in Applied Mathematics from University of Cologne
(2002). She has developed numerical simulations in the application fields of meteorol
ogical data
assimilation and grain growth simulation for polycrystals. She has experience in
modelling

and
simulation in virtual reality, visualization of scientific data and differential and parametric
optimization. After receiving her PhD she was respons
ible for scientific coordination at the
Fraunhofer Institute for Algorithms and Scientific Computing. In 2006 she spent 3.5 years at
Carnegie Mellon University (Pittsburgh, USA). There she worked in an interdisciplinary team in the
area of material science

simulation. Eva Eggeling is head of the business area Visual Computing of
Fraunhofer Austria.

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Dr. techn. Torsten Ullrich

has a PhD in computer science from Graz University of Technology
(2011)
and an MSc in mathematics from the Karlsruhe Institute of Tech
nology. His main research
areas are generative modelling, geometric reconstruction

and optimization
.
He combined these
topics in his PhD
-
thesis
“Reconstructive Geometry”
to an inverse optimization approach, which
uses generative models for reconstruction p
urposes.



2.2.2

University of Brighton

Organization profile

The University of Brighton is a public teaching and research university, with approximately 2100
staff and 19000 students spread across 4 campuses along the south coast of England. It is one of the
le
ading ‘post
-
1992’ universities for research in the UK, with a strong research culture in computer
science and cultural informatics. The Cultural Informatics Research Group was coordinator of both
the EPOCH Network of Excellence in FP6 and the 3D
-
COFORM IP
in FP7, and a partner in
CHIRON, a Marie Curie Research Network in Cultural Heritage Informatics. The Natural Language
Technology Group has a track record of high quality research in natural language processing
stretching back to the early 1990’s. The grou
p’s research interests include text analysis, text
generation and linguistic knowledge representation, encompassing traditional formal language
methods (grammars etc.), and modern statistical approaches to language processing.


Key competences and role in

the project

The technical focus of the Cultural Informatics group’s work is in bespoke 3D modelling and
rendering systems, led by Dr Karina Rodriguez
-
Echavarria. The group also led the development of
the EPOCH Network of Expertise Centres and EPOCH’s wor
k in the area of assessing socio
-
economic impact, which was reported in the EPOCH final review report as a highlight making
“ground
-
breaking progress in developing innovative methods and theory in the economics of the
cultural heritage”. This work has cont
inued to be developed by Dr Jaime Kaminski under the 3D
-
COFORM project, including the establishment and hosting of VCC
-
3D, the Virtual Competence
Centre for 3D Technology, a not
-
for
-
profit enterprise venture to promote and support the
exploitation of 3D te
chnologies. The Natural Language Technology Group’s expertise in

language
processing has increasingly been applied to the processing of metadata and textual descriptions
associated with Cultural Collections (led by Dr Roger Evans).The group has also
started exploring
the application of the same formal language techniques to problem of shape definitions, particular
in the context of procedural shape modelling systems.


CVs of key personnel

Professor David Arnold

is Director of Research Initiatives for
the University and Dean of the
Brighton Doctoral College. He is also Professor of Computing Science and was coordinator of both
EPOCH and 3D
-
COFORM. He has a 40
-
year career of research in the design of interactive
computer graphics systems and their applic
ations in architecture, engineering, cartography,
scientific visualisation, health and most recently cultural heritage. Whilst at UEA, Norwich he led
that University's contributions to a number of EU projects (including CHARISMATIC). He was the
founding Ed
itor
-
in
-
Chief of the ACM Journal on Computing and Cultural Heritage and has been
involved at a senior level in ACM, Eurographics, CEPIS and BCS. He has been one of the leaders
of the steering group that has established the VAST series of conferences over t
he past 12 years.

Dr Roger Evans

is a Reader in Computer Science in the School of Computing, Engineering and
Mathematics, and research team leader in NLTG. He has over 22 years post
-
doctoral research and
management experience, spanning text analysis and ge
neration, lexical representation and
architectures for natural language processing. He is a former SERC Advanced Fellow, a member of
the EPSRC College, a senior visiting research fellow at the University of Sussex, and former chair
of ACL
-
SIGGEN, the inter
national SIG for Natural Language Generation. He has been PI on 8
EPSRC/SERC grants, coordinator of a 9
-
site EU (INTAS) project, site coordinator for 4 other EU
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and international projects. He has been involved in the 3D
-
COFORM project contributing to the
d
evelopment of metadata extraction and manipulation tools.

Dr Karina Rodriguez
-
Echavarria

was awarded a PhD for her work on "Knowledge based
systems to support collaborative product development". She obtained her MA in Histories and
Culture at the Universit
y of Brighton in 2008. Karina works in the 3D
-
COFORM integrating project
in the area of 3D visualisation and the deployment of these technologies in cultural heritage
organisations. She previously participated in the European Network of Excellence EPOCH. H
er
research interests include the documentation and visualisation of heritage collections, information
and knowledge management of 3D artefacts, and the practical aspects of deployment in the heritage
sector.

Dr Jaime Kaminski

is an interdisciplinary resea
rcher who began his career as an archaeologist. He
has a Ph.D. from the University of Reading which considered the archaeological evidence for
ancient environmental impact. He left archaeology in 1996 to become an analyst in a technology
research company,
where he edited over 50 consultancy reports on technology issues and ICT
implementation in large enterprises. As a technology analyst he has undertaken freelance research
and consultancy projects for government, and other public and private organisations i
nternationally.
Since 2004 Jaime has worked at the University of Brighton's Business School. Initially his research
focused on the socio
-
economic impact of cultural heritage sites, with specific reference the impact
of ICT on those sites as part of the Eur
opean Commission
-
funded EPOCH Network of Excellence
and he leads the University’s contribution to the V
-
MUST NoE. Since 2008 he has worked on the
EC's 3D
-
COFORM project where he is a work
-
package leader. In this role he brings together
research on socio
-
ec
onomic impact, business modelling and sustainability as applied to the pipeline
of 3D data acquisition and visualisation. As part of this research activity he has co
-
developed
numerous impact assessment and strategy models for heritage. Furthermore, he has

conducted a
great deal of research and consultancy in the field of social enterprise; in doing so becoming the
University's first 'Commercial Fellow' in Social Enterprise.

2.2.3

Technische Universität Graz

Organization profile

Graz Technical University was foun
ded 1811 and is the second biggest technical university in
Austria with about 11.000 students in all fields of engineering. The Institute of ComputerGraphics
and KnowledgeVisualization (CGV) was founded in 2005 as part of the Faculty of Informatics. Its
mi
ssion and research focus is to link 3D data with semantic information in various fields, ranging
from virtual reality the over geometry processing and shape modelling to and digital libraries. CGV
has built its own Definitely Affordable Virtual Environment

(DAVE), one of the largest CAVE
systems in Austria. The team of eleven PhD studentds and two post
-
doc researchers is led by Prof.
Dieter Fellner. Recent projects are German DFG funded (PROBADO on digital libraries), Austrian
FFG funded (Metadesigner on pr
oduct mass customization, CITYFIT on procedural urban
reconstruction, AUTOVISTA on 3D surveillance), and EU funded (AGNES and V2me in ambient
assisted living, EPOCH and 3D
-
COFORM in Cultural Heritage), as well as industry projects
(SurfaceReconstruction pr
oject with Volkswagen AG). One of the core competencies of CGV,
which will be important in SHAPEATLAS, is procedural shape modelling on the basis of the
Generative Modeling Language. CGV is hosting the GML homepage un
der www.generative
-
modeling.org
.



Key
competences and role in the project

In SHAPEATLAS, partner TUG will contribute its long
-
standing expertise in procedural shape
modelling. The Generative Modeling Language (GML, see www.generative
-
modeling.org) is a
programming language for shape that was a
pplied and extended in various research projects. Its
distinctive feature is that it greatly facilitates automatic code generation, which makes it ideal, e.g.,
for capturing the construction history of a complex 3D model during interactive shape modelling
in
form of an executable script. So the end result of the modelling process is not just a mesh or a set of
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NURBS patches, but the shape construction history itself. It can be re
-
evaluated to obtain a whole
collection of similar shapes. In SHAPEATLAS, howev
er, the parametric infrastructure of GML will
be used for the inverse problem, shape analysis. Shape maps are composed of hierarchical shape
descriptors, which can suitably be described using GML’s operator paradigm.



CVs of key personnel

Prof. Dieter Fel
lner

is full professor of computer science and the head of CGV at TU Graz, as well
as of GRIS at TU Darmstadt. Furthermore he is director of Fraunhofer IGD in Darmstadt, one of
Europe’s largest computer graphics groups with more than 100 full
-
time equivale
nt researchers.
Fellner has lead the Digital Library initiative V
3
D
2

of the German Research Foundation (DFG)
which featured 20+ research projects over eight years. Fellner has written two books, numerous
research papers, was leader of the Eurographics Asso
ciation, and is member of the editorial board
of various journals and conferences. He initiated and actively pursues the notion of 3D models as
generalized documents, which is the conceptual basis leading to SHAPEATLAS.


Dr. Sven Havemann

is post
-
doc resea
rcher at CGV, leading the generative modelling group. He
graduated in Bonn university, got his PhD (with distinction) from Braunschweig Technical
University in 2005 and his Habilitation from TU Graz in 2012. His research interests range from
industrial sha
pe design and product mass customization over shape grammars and urban
reconstruction to virtual reality, advanced user interfaces, and shape repositories, typically in
connection with generative modelling. With more than ten years of experience in Cultura
l Heritage
projects (CHARISMATIC, EPOCH and 3D
-
COFORM) he will also contribute his knowledge of
practical problems in this sector, e.g., the management of large collections of similar shapes in a
repository of scanned shapes in a museum.


Martin Schröttner

is currently finishing his master’s thesis at CGV under the supervision of Sven
Havemann. While his thesis is on hierarchical radiosity, i.e., a rendering method for global
illumination, Schröttner was also deeply involved in 3D
-
COFORM. He is responsible
for
developing the
MetadataGenerator

tool for capturing process information of 3D
-
datasets in a
sustainable way using the CIDOC CRM standard, an entity
-
relationship model for semantic
networks.


2.2.4

Katholieke Universiteit Leuven

Organization profile

The tea
m at the Katholieke Universiteit Leuven that will work on the project is part of the Center
for the Processing of Speech and Images (PSI), one of the units within the department of Electrical
Engineering (ESAT). The VISICS team (VISion for Industry, Commun
ications, and Services)
-

specialises in computer vision and its applications, in 3D acquisition and modelling, and on object
and object class recognition.

The team has received several prizes for its work, including a David Marr award, two TechArt
prizes,

an EITC Technology Award, a Henry Ford prize and numeral best paper wards. It has
founded multiple spin
-
off companies: ICOS (chip inspection), Eyetronics (3D acquisition
technology for the games and movie industry mainly) and GeoAutomation (mobile mappin
g, i.e. 3D
measurements in cities from a moving van). It has been a partner in many European, Belgian and
Flemish projects.


Key competences and role in the project

The VISICS team has proed its expertise on 3D
-
digitisation and analysis for various applic
ations
ranging from ranging from s in the cultural heritage sector. Their research activities address all
aspects of 3D
-
capture, 3D
-
processing, the semantics of shape, material properties, metadata and
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provenance, integration with other sources (textual
and other media); search, research and
dissemination to the public and professional alike.

The work lies at the basis of several spin
-
off companies, bringing 3D to various application
domains, varying from industrial inspection, medical application, specia
l effects.


CVs of key personnel

Prof. Luc van Gool

heads both this group and the BIWI computer vision group at ETHZ in
Switzerland. He is an electrical engineer (University of Leuven) and a full professor. He is editor
-
in
-
chief of the journal Foundations
& Trends in Computer Graphics and Vision, and a member of
several editorial boards and IPCs for major conferences. He has been involved as program chair and
area chair of the main vision conferences ICCV, CVPR, and ECCV several times. He has been
program c
hair of ICCV05, general chair of ICCV11 and will be general chair of ECCV14.


Dr. Marc Proesmans

is
currently responsible for project and innovation coordination at KUL. He
is an electrical engineer (University of Leuven) and did his PhD in early visual pr
ocessing and 3D
reconstruction. The research results have spun off a company located in Los Angeles, specialized in
human scanning for movie / game VFX. He has over 20 years research, development and business
experience in the field of image processing and

3D scanning, and continues to perform and
supervise research activities on novel 3D acquisition techniques, with a specific interest for cultural
heritage. He has been involved in scanning for Sagalassos TR, V&A London, British Museum,
Tongeren, Brussels,

Berlin, LA, NY, etc.


2.2.5

Brunswick Town Charitable Trust

Organization profile

Key competences and role in the project

CVs of key personnel


Consortium as a
whole

2.3

Resources to be
committed

2.4





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3

Impact



Expected impacts listed in the work
programme

3.1

Dissemination and/or exploitation of project results, and management of intellectual
3.2
property





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4

Ethical
Issues





YES

PAGE

Informed Consent





Does the proposal involve children?





Does the proposal involve patients or persons not
able to
give consent?





Does the proposal involve adult healthy
volunteers?





Does the proposal involve Human Genetic
Material?





Does the proposal involve Human biological
samples?





Does the proposal involve Human data collection?



Research on Human
embryo/foetus





Does the proposal involve Human Embryos?





Does the proposal involve Human Foetal Tissue /
Cells?





Does the proposal involve Human Embryonic
Stem Cells?



Privacy





Does the proposal involve processing of genetic
information or
personal data (eg. health, sexual
lifestyle, ethnicity, political opinion, religious or
philosophical conviction)





Does the proposal involve tracking the location or
observation of people?



Research on Animals





Does the proposal involve research on
animals?





Are those animals transgenic small laboratory
animals?





Are those animals transgenic farm animals?





Are those animals cloned farm animals?





Are those animals non
-
human primates?



Research Involving Developing Countries





Use of local resources (genetic, animal, plant etc)





Impact on local community



Dual Use





Research having direct military application





Research having the potential for terrorist abuse



ICT Implants





Does the proposal involve clinical trials of ICT
implants?



I CONFIRM THAT NONE OF THE ABOVE ISSUES
APPLY TO MY PROPOSAL

X





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5

References


Akbar, S., K
ung, J., Wagner, R.: Multi
-
feature integration with relevance feedback on 3d model
similarity retrieval. J. Mob. Multimed. 3 (2007).


Arandjelovic, R., Zisserman, A.: Ecient image retrieval for 3D structures. In: Proc. BMVC (2010).


Arandjelovic, R., Zisse
rman, A.: Smooth object retrieval using a bag of boundaries. In: Proc. ICCV
(2011).


Bang, H.Y., Chen, T.: Feature space warping: an approach to relevance feedback. In: Intl. Conf.
Image Proc. Volume 1. (2002).


Bariya, P., Nishino, K.: Scale
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