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| RI | Thesis Oral | 31 Mar 2008 | |
Robotics Institute Thesis Oral 31 Mar 2008
Place and Time |
Abstract |
Further Details |
Thesis Committee
Studies in using image segmentation to improve object recognition
Caroline Pantofaru
Robotics Institute
Carnegie Mellon University
| Place and Time |
WEH 4623
9:30 AM
| Abstract |
Recognizing object classes is a central problem in computer vision, and
recently there has been renewed interest in also precisely localizing
objects with pixel-accurate masks. Since classes of deformable objects
can take a very large number of shapes in any given image, a requirement
for recognizing and generating masks for such objects is a method for
reducing the number of pixel sets which need to be examined. One method
for proposing accurate spatial support for objects and features is
data-driven pixel grouping through unsupervised image segmentation. The
goals of this thesis are to define and address the issues associated
with incorporating image segmentation into an object recognition framework.
The first part of this thesis examines the nature of image segmentation
and the implications for an object recognition system. We develop a
scheme for comparing and evaluating image segmentation algorithms which
includes the definition of criteria that an algorithm must satisfy to be
a useful black box, experiments for evaluating these criteria, and a
measure of automatic segmentation correctness versus human image
labeling. This evaluation scheme is used to perform experiments with
popular segmentation algorithms, the results of which motivate our work
in the remainder of this thesis.
The second part of this thesis explores approaches to incorporating the
regions generated by unsupervised image segmentation into an object
recognition framework. Influenced by our experiments with segmentation,
we propose principled methods for describing such regions. Given the
instability inherent in image segmentation, we experiment with
increasing robustness by integrating the information from multiple
segmentations. Finally, we examine the possibility of learning explicit
spatial relationships between regions. The efficacy of these techniques
is demonstrated on a number of challenging data sets.
| Further Details |
A copy of the thesis oral document can be found at http://www.cs.cmu.edu/~crp/thesis.pdf.
| Thesis Committee |
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