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Image Difference Threshold Strategies and Shadow Detection.
Author(s) -
PL Rosin,
TJ Ellis
Publication year - 1995
Publication title -
citeseer x (the pennsylvania state university)
Language(s) - English
Resource type - Conference proceedings
DOI - 10.5244/c.9.35
Subject(s) - artificial intelligence , thresholding , computer vision , shadow (psychology) , computer science , image (mathematics) , image texture , frame (networking) , object detection , contrast (vision) , motion detection , image quality , pattern recognition (psychology) , mathematics , image processing , motion (physics) , psychology , psychotherapist , telecommunications
The paper considers two problems associated with the detection and classification of motion in image sequences obtained from a static camera. Motion is detected by differencing a reference and the "current" image frame, and therefore requires a suitable reference image and the selection of an appropriate detection threshold. Several threshold selection methods are investigated, and an algorithm based on hysteresis thresholding is shown to give acceptably good results over a number of test image sets. The second part of the paper examines the problem of detecting shadow regions within the image which are associated with the object motion. This is based on the notion of a shadow as a semi-transpare nt region in the image which retains a (reduced contrast) representation of the underlying surface pattern, texture or grey value. The method uses a region growing algorithm which uses a growing criterion based on a fixed attenuation of the photometric gain over the shadow region, in comparison to the reference image.

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