Algorithmic issues in visual object recognition
Algorithmic issues in visual object recognition
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Date
2009
Authors
Hussein, Mohamed Elsayed Ahmed
Advisor
Davis, Larry
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Abstract
This thesis is divided into two parts covering two aspects of
research in the area of visual object recognition.
Part I is about human detection in still images. Human
detection is a challenging computer vision task due to the wide
variability in human visual appearances and body poses. In this
part, we present several enhancements to human detection
algorithms. First, we present an extension to the integral
images framework to allow for constant time computation of
non-uniformly weighted summations over rectangular regions
using a bundle of integral images. Such computational element
is commonly used in constructing gradient-based feature
descriptors, which are the most successful in shape-based human
detection. Second, we introduce deformable features as an
alternative to the conventional static features used in
classifiers based on boosted ensembles. Deformable features can
enhance the accuracy of human detection by adapting to pose
changes that can be described as translations of body features.
Third, we present a comprehensive evaluation framework for
cascade-based human detectors. The presented framework
facilitates comparison between cascade-based detection
algorithms, provides a confidence measure for result, and
deploys a practical evaluation scenario.
Part II explores the possibilities of enhancing the speed of
core algorithms used in visual object recognition using the
computing capabilities of Graphics Processing Units (GPUs).
First, we present an implementation of Graph Cut on GPUs, which
achieves up to 4x speedup against compared to a CPU
implementation. The Graph Cut algorithm has many applications
related to visual object recognition such as segmentation and
3D point matching. Second, we present an efficient sparse
approximation of kernel matrices for GPUs that can
significantly speed up kernel based learning algorithms, which
are widely used in object detection and recognition. We present
an implementation of the Affinity Propagation clustering
algorithm based on this representation, which is about 6 times
faster than another GPU implementation based on a conventional
sparse matrix representation.