Biomimetic Object Recognition

builderanthologyAI and Robotics

Oct 19, 2013 (3 years and 8 months ago)

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Computer Vision &

Biomimetic Object Recognition

Bruce A. Draper

Department of
Computer Science

January 28, 2008

Background : Computer Vision


The computer vision community specializes in the
interpretation of image data


3D reconstruction


Stereo analysis (up to N cameras)


Motion analysis


Includes image stabilization, image mosaicing, control


Mapping & Measurement


Object recognition


Model based


Knowledge based


Learned (supervised or unsupervised)


Traditionally funded by the military, but the domain of
applications is expanding


Computer Vision Resources


CVPR & related conferences since 1983 (PRIP 1977
-
82)


Hosted 1999 CVPR in Ft. Collins


ICCV, ECCV, ACCV, ICPR, ICVS, …


Technical Committee of the IEEE (PAMI)


Journals


IEEE Trans. On Pattern Analysis and Machine Intelligence (PAMI)


Computer Vision and Image Understanding (CVIU)


International Journal of Computer Vision (IJCV)


Machine Vision and Applications (MVA)


IEEE Trans. on Image Processing (TIP)


Pattern Recognition


On
-
line tools and resources


CVOnline (web site resource)


OpenCV (open library of computer vision algorithms)


Background : Personal


Object recognition


Knowledge
-
based & learned


Applications


Face recognition


Evaluation of face recognition algorithms & covariates


With R. Beveridge (CS), G. Givens (Stats)


Modeling faces as hihg dimensional manifolds


With M. Kirby (Math), C. Peterson (Math), R. Beveridge (CS)


Landmark recognition for self
-
driving cars


Visual
where am I?


Automatic population of geospatial data bases


Build semantic & temporal maps from satellite images


Biologically
-
inspired Cognitive Architectures (DARPA BICA)


With S. Kosslyn (Harvard)


Counting nesting seagulls on islands off the coast of Maine

What is this?

Dirty little secret: computer vision systems can’t
do this yet (not in general)

Well, there’s a truck,

driving over some rocks,
with mountains in the
background

My goal


Learn

to recognize objects
by mimicing
human vision


At the level of regional functional anatomy


End
-
to
-
end systems that work!


Three examples of how human vision
influences design:

1.
Selective attention

2.
Familiarity detection

3.
Goal
-
directed object detection

Selective Attention


Human vision is selective


Overt attention : eye & head movements


Covert attention : internal data selection

Familiarity vs Recognition


People recognize whether an image is
familiar

before they
recognize

what it is

So we show our system (SeeAsYou) a series of images…

Familiarity vs Recognition (II)


Then we give it new images, and ask it to
retrieve “similar” images from the data set



Novel Image

Retrieved Image

More examples





Next… recognition


Did we recognize the leopard on the previous
slide?


No, the answer was an image, not symbolic


Did we match the leopard image?


Depends: we matched it to a cheetah


If the goal was to match
spotted cats
(or wildlife,
or …), we got it right


If the goal was to find
leopards
, then no.


Current research : top
-
down verification of
specific goals based on evidential reasoning


Looking for new applications


Image inspection tasks currently done by
humans


Rule of thumb : if people can’t do it, neither can
our system


Object recognition


Not just measurement


Lots of data, limited training labels

Thank You

Questions?