Abstract
When processing an image of multiple occluded humans, segmenting them is a prerequisite for higher-level tasks such as tracking and activity analysis. Although a human observer can easily segment target humans partly occluded among themselves in an image, automatic segmentation in computer vision is difficult. In this paper, the use of a probabilistic neural network is proposed to learn various outline shape patterns of a foreground image blob of occluded humans, and then to segment the blob into its constituents. The segmentation is here regarded as a two-class pattern recognition problem; segmentable positions constitute a class and other positions constitute the other. The technique proposed is useful particularly for low-resolution images where existing image analysis techniques are difficult to be applied.
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© 2006 Springer-Verlag Berlin Heidelberg
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Do, Y. (2006). Segmenting Images of Occluded Humans Using a Probabilistic Neural Network. In: Huang, DS., Li, K., Irwin, G.W. (eds) Intelligent Computing. ICIC 2006. Lecture Notes in Computer Science, vol 4113. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11816157_56
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DOI: https://doi.org/10.1007/11816157_56
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-37271-4
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