Software
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Real-Time Pattern Matching using Projection Kernels
Abstract:
This technique suggests a novel approach to pattern matching which
reduces time complexity by two orders of magnitude compared to traditional
approaches. The approach uses an efficient projection scheme which bounds the distance
between a pattern and an image window using very few operations. The projection
framework is combined with a rejection scheme which allows rapid rejection of
image windows that are distant from the pattern. The approach was shown to be
effective even under very noisy conditions.
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Y.
Hel-Or and H. Hel-Or: Real Time
Pattern Matching using Projection Kernels, PAMI,
Sept 2005.
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Power Point
presentation.
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README
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Source code in C
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The Gray-Code Filter Kernels
Abstract:
We introduce a family of filter kernels—the Gray-Code Kernels (GCK)
and demonstrate their use in image analysis. Filtering an image with a
sequence of Gray-Code Kernels is highly efficient and requires only two
operations per pixel for each filter kernel, independent of the size or
dimension of the kernel. We show that the family of kernels is large and
includes the Walsh-Hadamard kernels, among others. The GCK can be used to
approximate any desired kernel and, as such forms, a complete
representation. The efficiency of computation using a sequence of GCK
filters can be exploited for various real-time applications, such as,
pattern detection, feature extraction, texture analysis, texture synthesis,
and more.
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G.
Ben-Artzi, H. Hel-Or, and Y. Hel-Or :
The Gray-Code Filter Kernels
IEEE Trans. Pattern
Analysis and Machine Intelligence, Vol
29, No. 3, March 2007
- C
code written by Josh Allmann
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Abstract:
We propose a fast pattern matching scheme termed Matching by Tone
Mapping (MTM) which allows matching under non-linear tone mappings. We
show that when tone mapping is approximates by a piecewise constant function
fast computational scheme is possible requiring computational time similar
to the fast implementation of Normalized Cross Correlation (NCC). In fact,
the MTM measure can be viewed as a generalization of the NCC for non-linear
mappings and actually reduces to NCC when mappings are restricted to be
linear. The MTM is shown to be invariant to non-linear tone mappings, and is
empirically shown to be highly discriminative and robust to noise.