| The Expert Object Recognition Pathway |
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Brain imaging studies suggest that expert object recognition is a distinct visual
skill, implemented by a dedicated anatomic pathway. Like all visual pathways,
the expert recognition pathway begins with the early visual system (retina,
LGN/SC, striate cortex). It is defined, however, by subsequent diffuse
activation in the lateral occipital cortex (LOC), and sharp foci of activation in
the fusiform gyrus and right inferior frontal gyrus. This pathway recognizes
familiar objects from familiar viewpoints under familiar illumination.
Significantly, it identifies objects at both the categorical and instance
(subcategorical) levels, and these processes cannot be disassociated. The system
presents a four-stage functional model of the expert object recognition pathway,
where each stage models one area of anatomic activation. It implements this
model in an end-to-end computer vision system, and tests it on real images to
provide feedback for the cognitive science and computer vision communities.
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| SAFE - Selective Attention as a Front End |
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Computational selective attention ssytems have mostly
been developed as models of human attention, and they have been
evaluated on that basis. Now, hoever, they are being used as front
ends to object recognition systems. As such, they need to be evaluated
by other criteria. A common goal for object recognition systems in invariance to
2D similarity transforms (i.e. in-plane translations, rotations,
reflections and scales). This implies that attention systems used
as front ends should also be invariant to similarity transforms. SAFE
is largely invariant to 2D similarity transformations of the source image
and selects scales as well as spatial locations for fixations, implementing a combined
"zoom-spotlight" model of attention.
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| HumanID - The CSU Face Identification System |
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The CSU Face Identification Evaluation System provides standard
face recognition algorithms and standard statistical methods for comparing face
recognition algorithms. The system includes standardized image pre-processing
software, three distinct face recognition algorithms, analysis software to study
algorithm performance, and Unix shell scripts to run standard experiments. All
code is written in ANSI C. The preprocessing code replicates feature of preprocessing
used in the FERET evaluations. The three algorithms provided are
Principle Components Analysis (PCA), a.k.a Eigenfaces, a combined Principle
Components Analysis and Linear Discriminant Analysis algorithm (PCA+LDA),
and a Bayesian Intrapersonal/Extrapersonal Classifier (BIC). The PCA+LDA and
BIC algorithms are based upon algorithms used in the FERET study contributed
by the University of Maryland and MIT respectively. There are two analysis. The
first takes as input a set of probe images, a set of gallery images, and similarity
matrix produced by one of the three algorithms. It generates a Cumulative Match
Curve of recognition rate versus recognition rank. The second analysis tool generates
a sample probability distribution for recognition rate at recognition rank
1, 2, etc. It takes as input multiple images per subject, and uses Monte Carlo
sampling in the space of possible probe and gallery choices. This procedure will,
among other things, add standard error bars to a Cumulative Match Curve. The
System is available through our website and we hope it will be used by others
to rigorously compare novel face identification algorithms to standard algorithms
using a common implementation and known comparison techniques.
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