Recognition and matching in the presence of deformation and lighting change
Recognition and matching in the presence of deformation and lighting change
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Date
2008-11-21
Authors
Sheorey, Sameer
Advisor
Jacobs, David W
Citation
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Abstract
Natural images of objects and scenes show a fascinating amount of
variability due to different factors like lighting and viewpoint change,
occlusion, articulation and non-rigid deformation. There are certain cases
like recognition of specular objects and images with arbitrary deformations
where existing techniques do not perform well. For image deformation, we
propose a method for faster keypoint matching with histogram descriptors and a
completely deformation invariant representation. We also propose a method for
improving specular object recognition.
Histograms are a powerful statistical representation for keypoint matching
and content based image retrieval. The <EM>earth mover's distance</EM> (EMD) is an
important perceptually meaningful metric for comparing histograms, but it
suffers from high (O(n<SUP>3</SUP> log n)) computational complexity. We
propose a novel <em>linear time</em> algorithm for approximating EMD with the
weighted L<SUB>1</SUB> norm of the wavelet transform of the difference
histogram. We prove that the resulting wavelet EMD metric is equivalent to
EMD. We experimentally show that wavelet EMD is a good approximation to EMD,
has similar performance, but requires much less computation. We also give a
fast algorithm for the best partial EMD match between two histograms.
Images of non-planar object can undergo a large non-linear deformation due
to a viewpoint change. Complex deformations occur in images of non-rigid
objects, for example, in medical image sequences. We propose using the
<EM>contour tree</EM> as a novel framework invariant to arbitrary deformations for
representing and comparing images. It represents all the deformation invariant
information in an image.
Lighting changes greatly affect the appearance of <EM>specular</EM> objects and
make recognition difficult much more than for Lambertian objects. In model
based recognition of specular objects, an important constraint is that the
estimated lighting should be non-negative everywhere. We propose a new method
to enforce this constraint and explore its usefulness in specular object
recognition, using the spherical harmonic representation of lighting. The new
method is faster as well as more accurate than previous methods. Experiments
on both synthetic and real data indicate that the constraint can improve
recognition of specular objects by better separating the correct and incorrect
models.