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Proceedings Paper

Improvement of object classification in image sequences
Author(s): D. Ernst; H. Gross; D. Stricker; Ulrich Thoennessen
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Paper Abstract

It is known that the performance of recognition by humans is improved by time integration. We have investigated this phenomenon in image sequences. The object hypotheses are detected in the image sequence utilizing object regions and motion in analogy to human perception. A multiple thresholding segmentation and a change detection process by wavelet transformation are used for detection. The detected segments are tracked over time. Our classification approach assumes, that the objects are recognized as a connected entity. Therefore a structural object description derived from the image sequence was developed. This description contains the geometric relations and shape features of the individual object parts as well as the motion behavior. Normalized difference measures of the structural descriptions have been derived for the classification. The differences are determined and combined by a fuzzy approach. The results have shown, that the classification can be improved and stabilized by the object description derived from the image sequence.

Paper Details

Date Published: 23 June 1997
PDF: 10 pages
Proc. SPIE 3069, Automatic Target Recognition VII, (23 June 1997); doi: 10.1117/12.277128
Show Author Affiliations
D. Ernst, FGAN/FIM (Germany)
H. Gross, FGAN/FIM (Germany)
D. Stricker, FGAN/FIM (Germany)
Ulrich Thoennessen, FGAN/FIM (Germany)


Published in SPIE Proceedings Vol. 3069:
Automatic Target Recognition VII
Firooz A. Sadjadi, Editor(s)

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