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Proceedings Paper

Motion analysis and classification with directional Gaussian derivatives in image sequences
Author(s): Boris Escalante-Ramirez; Jose Luis Silvan-Cardenas
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Paper Abstract

This work is intended to provide some ideas on the use of a Gaussian-derivative model for visual perception, called the Hermite transform, to extract motion information from an image sequence. Gaussian-derivative operators have long been used in computer vision for feature extraction and are relevant in visual system modeling. A directional energy is defined in terms of the 1-D Hermite transform coefficients of local projections. Each projection is described by the Hermite transform, resulting in a directional derivative analysis of the input at a given spatiotemporal scale. We demonstrate that the 1-D Hermite transform coefficients of local projections are readily computed as a linear mapping of the 3-D Hermite transform coefficients through some projecting functions. The directional response is used to detect spatiotemporal patterns that are 1-D or 2-D. Practical consideration and experimental results are also of concern.

Paper Details

Date Published: 13 November 2000
PDF: 7 pages
Proc. SPIE 4116, Advanced Signal Processing Algorithms, Architectures, and Implementations X, (13 November 2000); doi: 10.1117/12.406524
Show Author Affiliations
Boris Escalante-Ramirez, Univ. Nacional Autonoma de Mexico (Mexico)
Jose Luis Silvan-Cardenas, Univ. Nacional Autonoma de Mexico (Mexico)


Published in SPIE Proceedings Vol. 4116:
Advanced Signal Processing Algorithms, Architectures, and Implementations X
Franklin T. Luk, Editor(s)

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