Segmenting images of occluded humans using a probabilistic neural network

Authors:
Yongtae Do
Affiliations:
School of Electronic Engineering, Daegu University, Gyeongsan-City, Gyeongbuk, South Korea
Venue:
ICIC'06 Proceedings of the 2006 international conference on Intelligent Computing - Volume Part I
Year:
2006

Citing 6
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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.