image processing - Global Soft Solutions

breezebongAI and Robotics

Nov 6, 2013 (3 years and 7 months ago)

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IMAGE PROCESSING

1.

A PRIMAL

DUAL METHOD FOR T
OTAL
-
VARIATION
-
BASED
WAVELET DOMAIN INPAINTING

Loss of information in
a

wavelet

domain

can occur during storage or transmission
when the images are formatted and stored in terms of
wavelet

coefficients.
This calls
for

image
inpainting

in
wavelet

domains
.
In this paper,
a

variational
approach is used to formulate the reconstruction problem. We propose
a

simple
but very efficient iterative scheme to calculate an optimal solution and prove its
convergence. Numerical results are presented to show the performan
ce of the
proposed algorithm.


2.

A SECRET
-
SHARING
-
BASED METHOD FOR AUTHENTICATION OF
GRAYSCALE DOCUMENT IMAGES VIA THE USE OF THE PNG
IMAGE WITH A DATA REPAIR CAPABILITY


A

new blind
authentication

method

based

on
the

secret

sharing

technique
w
ith
a

data repair capability
for

grayscale

document

images

via

the

use

of

the

Portable Network Graphics (
PNG
)
image

is proposed. An
authentication

signal is generated
for

each block
of

a

grayscale

document

image
, which,
together with
the

binarized block co
ntent, is transformed into several
shares
using

the

Shamir
secret

sharing

scheme.
The

involved parameters are
carefully chosen so that as many shares as possible are generated and
embedded into an alpha channel plane.
The

alpha channel plane is then
combin
ed with
the

original
grayscale

image

to form
a

PNG

image
. During
the

embedding process,
the

computed share values are mapped into
a

range
of

alpha channel values near their maximum value
of

255 to yield
a

transparent
stego
-
image

with
a

disguise effect. In
the

process
of

image

authentication
,
an
image

block is marked as tampered if
the

authentication

signal computed
from
the

current block content does not match that extracted from
the

shares embedded in
the

alpha channel plane. Data repairing is then applied

to
each tampered block by
a

reverse Shamir scheme after collecting two
shares from unmarked blocks. Measures
for

protecting
the

security
of

the

data hidden in
the

alpha channel are also proposed. Good experimental
results prove
the

effectiveness
of

the

pr
oposed
method

for

real
applications.


3.

IMAGE REDUCTION USING MEANS O
N DISCRETE PRODUCT
LATTICES



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We investigate the problem of averaging values
on

lattices

and, in particular,
on

discrete

product

lattices
. This problem arises in
image

processing when
several color values given in RGB, HSL, or another coding scheme need to

be
combined. We show how the arithmetic
mean

and the median can be
constructed by minimizing appropriate penalties, and we discuss which of
them coincide with the Cartesian
product

of the standard
mean

and the
median. We apply these functions in
image

pro
cessing. We present three
algorithms for color
image

reduction

based
on

minimizing penalty functions
on

discrete

product

lattices
.


4.

VEHICLE DETECTION IN AERIAL SURVEILLANCE USING
DYNAMIC BAYESIAN NETWORKS


We present an automatic
vehicle

detection

system for
aerial

surveillance

in

this paper.
In

this syst
em, we escape from the stereotype and existing
frameworks of
vehicle

detection

in

aerial

surveillance
, which are either
region based or sliding window based. We design a pixelwise classification
method for
vehicle

detection
. The novelty lies
in

the fact th
at,
in

spite of
performing pixelwise classification, relations among neighboring pixels
in

a
region are preserved
in

the feature extraction process. We consider
features including
vehicle

colors and local features. For
vehicle

color
extraction, we utilize
a color transform to separate
vehicle

colors and
nonvehicle colors effectively. For edge
detection
, we apply moment
preserving to adjust the thresholds of the Canny edge detector
automatically, which increases the adaptability and the accuracy for
detectio
n

in

various
aerial

images. Afterward, a
dynamic

Bayesian

network

(DBN) is constructed for the classification purpose. We convert regional
local features into quantitative observations that can be referenced when
applying pixelwise classification via DBN.
Experiments were conducted on a
wide variety of
aerial

videos. The results demonstrate flexibility and good
generalization abilities of the proposed method on a challenging data set
with
aerial

surveillance

images taken at different heights and under
diffe
rent camera angles.




5.

ABRUPT MOTION TRACK
ING VIA INTENSIVELY ADAPTIVE
MARKOV
-
CHAIN MONTE CARLO SAMPLING



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The robust
tracking

of
abrupt

motion

is a challenging task in computer vision
due to its large
motion

uncertainty. While various particle filters and
conventional
Markov
-
chain

Monte

Carlo

(MC
MC) methods have been proposed
for visual
tracking
, these methods often suffer from the well
-
known local
-
trap problem or from poor convergence rate. In this paper, we propose a
novel
sampling
-
based
tracking

scheme for the
abrupt

motion

problem in the
Bayes
ian filtering framework. To effectively handle the local
-
trap problem,
we first introduce the stochastic approximation
Monte

Carlo

(SAMC)
sampling

method into the Bayesian filter
tracking

framework, in which the
filtering distribution is adaptively estimat
ed as the
sampling

proceeds, and
thus, a good approximation to the target distribution is achieved. In
addition, we propose a new MCMC sampler with intensive adaptation to
further improve the
sampling

efficiency, which combines a density
-
grid
-
based predict
ive model with the SAMC
sampling
, to give a proposal
adaptation scheme. The proposed method is effective and computationally
efficient in addressing the
abrupt

motion

problem. We compare our approach
with several alternative
tracking

algorithms, and extens
ive experimental
results are presented to demonstrate the effectiveness and the efficiency
of the proposed method in dealing with various types of
abrupt

motions
.


6.

IMAGE RESTORATION BY MATCHING GRADIENT
DISTRIBUTIONS

The
restoration

of a blurry or noisy
image

is commonly performed with a
MAP estimator, which maximizes a post
erior probability to reconstruct a
clean
image

from a degraded
image
. A MAP estimator, when used with a
sparse
gradient

image

prior, reconstructs piecewise smooth
images

and
typically removes textures that are important for visual realism. We present
an al
ternative deconvolution method called iterative
distribution

reweighting
(IDR) which imposes a global constraint on
gradients

so that a reconstructed
image

should have a
gradient

distribution

similar to a reference
distribution
.
In natural
images
, a refere
nce
distribution

not only varies from one
image

to
another, but also within an
image

depending on texture. We estimate a
reference
distribution

directly from an input
image

for each texture
segment. Our algorithm is able to restore rich mid
-
frequency textu
res. A
large
-
scale user study supports the conclusion that our algorithm improves
the visual realism of reconstructed
images

compared to those of MAP
estimators.


7.

PROTOTYPE
-
BASED IMAGE SEARCH RERANKING


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The existing methods for
image

search

reranking

suffer from the
unreliability of the assumptions under which the initial text
-
based

image

search

result is employed in the
reranking

process. In this paper, we propose
a
prototype
-
based

reranking

method to address this problem in a supervised,
but scalable fashion. The typical assumption that the top
-
N
images

in the
text
-
based

search

result are

equally relevant is relaxed by linking the
relevance of the
images

to their initial rank positions. Then, we employ a
number of
images

from the initial
search

result as the prototypes that serve
to visually represent the query and that are subsequently us
ed to construct
meta rerankers. By applying different meta rerankers to an
image

from the
initial result,
reranking

scores are generated, which are then aggregated
using a linear model to produce the final relevance score and the new rank
position for an
i
mage

in the reranked
search

result. Human supervision is
introduced to learn the model weights offline, prior to the online
reranking

process. While model learning requires manual labeling of the results for a
few queries, the resulting model is query inde
pendent and therefore
applicable to any other query. The experimental results on a representative
web
image

search

dataset comprising 353 queries demonstrate that the
proposed method outperforms the existing supervised and unsupervised
reranking

approaches
. Moreover, it improves the performance over the text
-
based

image

search

engine by more than 25.48%.


8.

SEGMENTING HUMAN FROM PHOTO IMAGES BASED ON A
COARSE
-
TO
-
FINE SCHEME


Human

segmentation in
photo

images

is
a

challenging and important problem
that finds numerous applications ranging
from

album making and
pho
to

classification
to

image

retrieval. Previous works
on

human

segmentation
usually demand
a

time
-
consuming training phase for complex shape
-
matching
processes. In this paper, we propose
a

straightforward framework
to

automatically recover
human

bodies
from

color
photos
. Employing
a

coarse
-
to
-
fine

strategy, we first detect
a

coarse

torso (CT) using the multicue CT
detection algorithm and then extract the accurate region of the upper body.
Then, an iterative multiple oblique histogram algorithm is presented
t
o

accurately recover the lower body
based

on

human

kinematics. The
performance of our algorithm is evaluated
on

our own data set (contains 197
images

with
human

body region ground truth data), VOC 2006, and the 2010
data set. Experimental results demonstra
te the merits of the proposed
method in
segmenting

a

person with various poses.



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9.

IMAGE DEBLURRING USING DERIVATIVE COMPRESSED SENSING
FOR OPTICAL IMAGING APPLICATION


The problem of reconstruction of digital
images

from their blurred and noisy
measurements is unarguably one of the central prob
lems in
imaging

sciences.
Despite its ill
-
posed nature, this problem can often be solved in a unique and
stable manner, provided appropriate assumptions on the nature of the
images

to be recovered. In this paper, however, a more challenging setting is
cons
idered, in which accurate knowledge of the blurring operator is lacking,
thereby transforming the reconstruction problem at hand into a problem of
blind deconvolution. As a specific
application
, the current presentation
focuses on reconstruction of short
-
e
xposure
optical

images

measured
through atmospheric turbulence. The latter is known to give rise to random
aberrations in the
optical

wavefront, which are in turn translated into
random variations of the point spread function of the
optical

system in
use
.
A standard way to track such variations involves
using

adaptive optics. Thus,
for

example, the Shack
-
Hartmann interferometer provides measurements of
the
optical

wavefront through
sensing

its partial
derivatives
. In such a case,
the accuracy of wavefront r
econstruction is proportional to the number of
lenslets
used

by the interferometer and, hence, to its complexity.
Accordingly, in this paper, we show how to minimize the above complexity
through reducing the number of the lenslets while compensating
for

un
dersampling artifacts by means of
derivative

compressed

sensing
.
Additionally, we provide empirical proof that the above simplification and its
associated solution scheme result in
image

reconstructions, whose quality is
comparable to the reconstructions o
btained
using

conventional (dense)
measurements of the
optical

wavefront.


10.

FUZZY LOCAL GAUSSIAN MIXTURE MODEL FOR BRAIN MR
IMAGE SEGMENTATION


Accurate
brain

tissue
segmentation

from magnetic resonance (
MR
)
images

is
an essential step in quantitative
brain

image

analysis. However, due to the
existence of noise
and intensity inhomogeneity in
brain

MR

images
, many
segmentation

algorithms suffer from limited accuracy. In this paper, we
assume that the
local

image

data within each voxel's neighborhood satisfy
the
Gaussian

mixture

model

(GMM), and thus propose the
fu
zzy

local

GMM
(FLGMM) algorithm
for

automated
brain

MR

image

segmentation
. This
algorithm estimates the
segmentation

result that maximizes the posterior
probability by minimizing an objective energy function, in which a truncated

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Gaussian

kernel function i
s used to impose the spatial constraint and
fuzzy

memberships are employed to balance the contribution of each GMM. We
compared our algorithm to state
-
of
-
the
-
art
segmentation

approaches in
both synthetic and clinical data. Our results show that the propose
d
algorithm can largely overcome the difficulties raised by noise, low contrast,
and bias field, and substantially improve the accuracy of
brain

MR

image

segmentation
.