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Contextual smoothing of image segmentation

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3 Author(s)
Letham, J. ; Heriot-Watt Univ., Edinburgh, UK ; Robertson, N.M. ; Connor, B.

This paper presents a new method for improving region segmentation in sequences of images when temporal and spatial prior context is available. The proposed technique uses elementary classifiers on infra-red, polarimetic and video data to obtain a coarse segmentation per-pixel. Contextual information is exploited in a Bayesian formulation to smooth the segmentation between frames. This is a general framework and significantly enhances segmentation from the classifiers alone. The method is demonstrated by classifying images of a rural scene into 3 positive classes: sky, vegetation and road, and one class of all other unlabelled data. Priors for the probabilistic smoothing in this scene are learned from ground-truth images. It is shown that an overall improvement of around 10% is achieved. Individual classes are improved by up to 30%.

Published in:

Computer Vision and Pattern Recognition Workshops (CVPRW), 2010 IEEE Computer Society Conference on

Date of Conference:

13-18 June 2010

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