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15 April 2010 The spatial vision tree: a generic pattern recognition engine: scientific foundations, design principles, and preliminary tree design
Zia-ur Rahman, Daniel J. Jobson, Glenn A. Woodell
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Proceedings Volume 7701, Visual Information Processing XIX; 77010K (2010) https://doi.org/10.1117/12.851869
Event: SPIE Defense, Security, and Sensing, 2010, Orlando, Florida, United States
Abstract
New foundational ideas are used to define a novel approach to generic visual pattern recognition. These ideas proceed from the starting point of the intrinsic equivalence of noise reduction and pattern recognition when noise reduction is taken to its theoretical limit of explicit matched filtering. This led us to think of the logical extension of sparse coding using basis function transforms for both de-noising and pattern recognition to the full pattern specificity of a lexicon of matched filter pattern templates. A key hypothesis is that such a lexicon can be constructed and is, in fact, a generic visual alphabet of spatial vision. Hence it provides a tractable solution for the design of a generic pattern recognition engine. Here we present the key scientific ideas, the basic design principles which emerge from these ideas, and a preliminary design of the Spatial Vision Tree (SVT). The latter is based upon a cryptographic approach whereby we measure a large aggregate estimate of the frequency of occurrence (FOO) for each pattern. These distributions are employed together with Hamming distance criteria to design a two-tier tree. Then using information theory, these same FOO distributions are used to define a precise method for pattern representation. Finally the experimental performance of the preliminary SVT on computer generated test images and complex natural images is assessed.
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Zia-ur Rahman, Daniel J. Jobson, and Glenn A. Woodell "The spatial vision tree: a generic pattern recognition engine: scientific foundations, design principles, and preliminary tree design", Proc. SPIE 7701, Visual Information Processing XIX, 77010K (15 April 2010); https://doi.org/10.1117/12.851869
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KEYWORDS
Pattern recognition
Visualization
Denoising
Image enhancement
Information theory
Visibility
Convolution
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