@conference {13194,
	title = {Extracting regions of symmetry},
	booktitle = {Image Processing, 2005. ICIP 2005. IEEE International Conference on},
	volume = {3},
	year = {2005},
	month = {2005/09//},
	pages = {III - 133-6 - III - 133-6},
	abstract = {This paper presents an approach for extending the normalized-cut (n-cut) segmentation algorithm to find symmetric regions present in natural images. We use an existing algorithm to quickly detect possible symmetries present in an image. The detected symmetries are then individually verified using the modified n-cut algorithm to eliminate spurious detections. The weights of the n-cut algorithm are modified so as to include both symmetric and spatial affinities. A global parameter is defined to model the tradeoff between spatial coherence and symmetry. Experimental results indicate that symmetric quality measure for a region segmented by our algorithm is a good indicator for the significance of the principal axis of symmetry.},
	keywords = {algorithm;, coherence;, DETECTION, detection;, elimination;, extraction;, feature, image, images;, natural, normalized-cut, object, region, segmentation, segmentation;, spatial, spurious, symmetry},
	doi = {10.1109/ICIP.2005.1530346},
	author = {Gupta,A. and Prasad,V. S.N and Davis, Larry S.}
}
@conference {13248,
	title = {Probabilistic tracking in joint feature-spatial spaces},
	booktitle = {Computer Vision and Pattern Recognition, 2003. Proceedings. 2003 IEEE Computer Society Conference on},
	volume = {1},
	year = {2003},
	month = {2003/06//},
	pages = {I-781 - I-788 vol.1 - I-781 - I-788 vol.1},
	abstract = {In this paper, we present a probabilistic framework for tracking regions based on their appearance. We exploit the feature-spatial distribution of a region representing an object as a probabilistic constraint to track that region over time. The tracking is achieved by maximizing a similarity-based objective function over transformation space given a nonparametric representation of the joint feature-spatial distribution. Such a representation imposes a probabilistic constraint on the region feature distribution coupled with the region structure, which yields an appearance tracker that is robust to small local deformations and partial occlusion. We present the approach for the general form of joint feature-spatial distributions and apply it to tracking with different types of image features including row intensity, color and image gradient.},
	keywords = {analysis;, appearance, appearance;, color;, colour, Computer, constraint;, deformation;, detection;, distribution;, edge, estimation;, extraction;, feature, feature-spatial, feature;, function, gradient;, image, intensity;, joint, likelihood, local, maximization;, maximum, nonparametric, object, objective, occlusion;, optical, partial, probabilistic, probability;, region, representation;, row, similarity-based, small, space;, structure;, target, tracker;, tracking;, transformation, vision;},
	doi = {10.1109/CVPR.2003.1211432},
	author = {Elgammal,A. and Duraiswami, Ramani and Davis, Larry S.}
}
@conference {13568,
	title = {Hybrid thinning through reconstruction},
	booktitle = {Document Analysis and Recognition, 1995., Proceedings of the Third International Conference on},
	volume = {2},
	year = {1995},
	month = {1995/08//},
	pages = {632 -635 vol.2 - 632 -635 vol.2},
	abstract = {One difficulty with many pixel-wise thinning algorithms is that they produce unacceptable results at junction points, or in the presence of contour noise. The authors present a novel approach to detecting ambiguous regions in a thinned image. The method uses the reconstructability properties of appropriate thinning algorithms to reverse the thinning process and automatically detect those pixels which may have resulted from more then one stroke in the image. The ambiguous regions are then interpreted and reconstructed using domain specific or derived contextual information. The approach has the advantage of using local methods to rapidly identify strokes (or regions) which have been thinned correctly and allowing more detailed analysis based on non-local methods in the remaining regions},
	keywords = {algorithms;edge, ambiguous, detection;automatic, detection;contextual, detection;image, information;contour, methods;nonlocal, methods;pixel-wise, noise;hybrid, Pixel, points;local, reconstruction;image, region, segmentation;noise;, stroke;junction, thinning, thinning;image},
	doi = {10.1109/ICDAR.1995.601975},
	author = {David Doermann and Kia,O.}
}
