[BIC-announce] FW: [Fwd: Ph.D Defense for Matthew Toews-A Probabilistic Model to Learn, Detect, Localize and Classify Patterns in Arbitrary Images

[Jennifer Chew, Ms.]

	The thesis defense of Mr. Matthew Toews has been scheduled to
take place  
	
	Monday April, 21, at 10:00 a.m. in   
	Room 603,  McConnell Engineering Building. 
	 
	 
	 
	A Probabilistic Model to Learn, Detect, Localize and Classify
	Patterns in Arbitrary Images
	

	Abstract

	This thesis presents a new, probabilistic model for describing
image patterns arising from classes of visually similar objects, such as
faces or brains. The model describes patterns in terms of a high level
geometrical structure referred to as an object class invariant (OCI),
which is invariant to nuisance parameters arising from the imaging
process. The OCI itself is not directly observed from images, but can be
inferred via a probabilistic model based on generic, spatially localized
image features. The OCI model can be learned from a large set of natural
images containing pattern instances with minimal manual supervision, in
the presence of background clutter, illumination changes, partial
pattern occlusion, multi-modal intra-pattern variation (e.g. faces with
or without sunglasses), geometrical deformations (i.e. translations,
rotations and magnifications) and viewpoint changes. In addition, it can
be automatically fit to new images in similar difficult imaging
conditions. Due to the general nature of the OCI model, it has a wide
range of possible applications, and its importance is demonstrated in
the research fields of computer vision and medical image analysis. In
computer vision, the OCI model results in the first viewpoint-invariant
system for detecting, localizing and classifying object instances in
terms of visual traits. Experimentation on face and motorcycle imagery
demonstrates the OCI model can be used to learn, detect and localize
general 3D object classes in natural imagery acquired from arbitrary
viewpoints. Viewpoint-invariant OCI detection performance is shown to be
superior to that of the multi-view formulation which models viewpoint
information explicitly. A data-driven algorithm demonstrates the
existence of stable OCIs, which can potentially be identified in a fully
automatic fashion. The first results in the literature are established
for sex classification of face images from arbitrary viewpoints and in
the presence of occlusion. In medical image analysis, the OCI model
results in the first parts-based anatomical model of the human brain,
where subject images of a population are described in terms of a collage
of conditionally independent local features or 'parts'. The model is the
first to explicitly address the situation where one-to-one
correspondence between different subjects does not exist due to natural
inter-subject variability. Experimentation modeling the human brain in
MR image slices demonstrates that the OCI model is capable of robustly
identifying and quantifying anatomical structures in terms of their
geometry, appearance, occurrence frequency and relationship to traits
such as sex in a population, in cases where other models cannot cope.
	




-------------- next part --------------
An HTML attachment was scrubbed...
URL: http://www2.bic.mni.mcgill.ca/pipermail/bic-announce/attachments/20080417/39b98a45/attachment.htm