Multiscale Vector Fields for Image Pattern Recognition

Kah-Chan Low; James M. Coggins

doi:10.1117/12.969731

1 March 1990 Multiscale Vector Fields for Image Pattern Recognition

Kah-Chan Low, James M. Coggins

Proceedings Volume 1192, Intelligent Robots and Computer Vision VIII: Algorithms and Techniques; (1990) https://doi.org/10.1117/12.969731
Event: 1989 Symposium on Visual Communications, Image Processing, and Intelligent Robotics Systems, 1989, Philadelphia, PA, United States

Abstract

We propose a uniform processing framework for low-level vision computing in which a bank of spatial filters maps the image intensity structure at each pixel into an abstract feature space. Some properties of the filters and the feature space will be described. Local orientation is measured by a vector sum in the feature space as follows: each filter's preferred orientation along with the strength of the filter's output determine the orientation and the length of a vector in the feature space; the vectors for all filters are summed to yield a resultant vector for a particular pixel and scale. The orientation of the resultant vector indicates the local orientation, and the magnitude of the vector indicates the strength of the local orientation preference. Limitations of the vector sum method will be discussed. Our investigations show that the processing framework provides a useful, redundant representation of image structure across orientation and scale.

Citation Download Citation

Kah-Chan Low and James M. Coggins "Multiscale Vector Fields for Image Pattern Recognition", Proc. SPIE 1192, Intelligent Robots and Computer Vision VIII: Algorithms and Techniques, (1 March 1990); https://doi.org/10.1117/12.969731

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