Affine invariant matching of noisy objects.
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Authors
Kao, Chang-Lung
Subjects
affine transformation
affine invariant matching
hashing
affine invariant matching
hashing
Advisors
Lee, Chin-Hwa
Date of Issue
1989-12
Date
December 1989
Publisher
Monterey, California. Naval Postgraduate School
Language
en_US
Abstract
In computer vision many techniques have been developed for object recognition.
The affine invariant matching algorithm proposed by Hummel and Wolfson (1988) is a
new and interesting method. Under affine invariant transformation, objects with translation,
rotation, scale changes, and, or even partial occlusion will have the same or similar
coefficients. However, some serious problems exist in the original algorithm.
This thesis begins with the discussion of the affine transformation. The shortcomings
that can occur in this method such as the basis instability, the collision of hash table,
and the noise sensitivity will be discussed. Among them the noise sensitivity is a
serious problem. This can always cause the recognition procedure to fail. In this thesis
an improved affine invariant matching algorithm was developed to overcome the noise
problem and other disadvantages of the original algorithm.
The area test criteria were adopted to avoid the numerical instability problem. The
modified hashing structure using a special hash function was implemented to achieve
faster accessing and voting. In the recognition procedure, the partial voting technique
with the consideration of false peaks from the voting array highly enhanced the noise
tolerance of the algorithm. Finally, the results obtained from the improved algorithm
clearly showed better performance than those of the original algorithm.
Type
Thesis
Description
Series/Report No
Department
Electrical and Computer Engineering
Organization
Naval Postgraduate School (U.S.)
Identifiers
NPS Report Number
Sponsors
Funder
Format
89 p.
Citation
Distribution Statement
Approved for public release; distribution is unlimited.
Rights
Copyright is reserved by the copyright owner