Machine vision: an incremental learning system based on features derived using fast Gabor transforms for the identification of textural objects

Richard M. Clark; Osei Adjei; Harpal Johal

doi:10.1117/12.447273

2 November 2001 Machine vision: an incremental learning system based on features derived using fast Gabor transforms for the identification of textural objects

Richard M. Clark, Osei Adjei, Harpal Johal

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Proceedings Volume 4476, Vision Geometry X; (2001) https://doi.org/10.1117/12.447273
Event: International Symposium on Optical Science and Technology, 2001, San Diego, CA, United States

Abstract

This paper proposes a fast, effective and also very adaptable incremental learning system for identifying textures based on features extracted from Gabor space. The Gabor transform is a useful technique for feature extraction since it exhibits properties that are similar to biologically visual sensory systems such as those found in the mammalian visual cortex. Although two-dimensional Gabor filters have been applied successfully to a variety of tasks such as text segmentation, object detection and fingerprint analysis, the work of this paper extends previous work by incorporating incremental learning to facilitate easier training. The proposed system transforms textural images into Gabor space and a non-linear threshold function is then applied to extract feature vectors that bear signatures of the textural images. The mean and variance of each training group is computed followed by a technique that uses the Kohonen network to cluster these features. The centers of these clusters form the basis of an incremental learning paradigm that allows new information to be integrated into the existing knowledge. A number of experiments are conducted for real-time identification or discrimination of textural images.

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Richard M. Clark, Osei Adjei, and Harpal Johal "Machine vision: an incremental learning system based on features derived using fast Gabor transforms for the identification of textural objects", Proc. SPIE 4476, Vision Geometry X, (2 November 2001); https://doi.org/10.1117/12.447273

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