Paper
6 October 1998 Genetic feature selection for highly accurate stereo reconstruction of natural surfaces
Author Affiliations +
Abstract
One approach to stereo matching is to use different local features to find correspondences. The selection of an optimum feature set is the content of this paper. An operational software tool based on the principle of comparing feature vectors is used for stereo matching. A relatively large set of different local features is sought for optimum combinations of 6 - 10 of them. This is done by a genetic process that uses an intrinsic quality criterion that evaluates the correctness of each individual match. The convergence of the genetic feature selection process is demonstrated on a real stereo pair of a tunnel surface. Four areas were used for individual optimization. After several hundred generations for each of the areas, it is shown that the identified feature sets result in a considerably better stereo matching result than the currently used features, which were the result of an initial manual choice. The experiments described in this paper use a `super-set' of 145 features for every pixel, which are created by filtering the image with convolution kernels (averaging, Gaussian filters, bandpass, highpass), median filters and Gabor kernels. From these 145 filters, the genetic feature selection process selects an optimal set of operators. Using the selected filters results in a 15% improvement of the matching accuracy and robustness.
© (1998) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Gerhard Paar, Oliver Sidla, and Wolfgang Poelzleitner "Genetic feature selection for highly accurate stereo reconstruction of natural surfaces", Proc. SPIE 3522, Intelligent Robots and Computer Vision XVII: Algorithms, Techniques, and Active Vision, (6 October 1998); https://doi.org/10.1117/12.325791
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Cited by 8 scholarly publications.
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KEYWORDS
Genetics

Feature selection

Image filtering

Digital filtering

Ions

Gaussian filters

Bandpass filters

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