@conference {13136,
	title = {Segmentation using Meta-texture Saliency},
	booktitle = {Computer Vision, 2007. ICCV 2007. IEEE 11th International Conference on},
	year = {2007},
	month = {2007/10//},
	pages = {1 - 8},
	abstract = {We address segmentation of an image into patches that have an underlying salient surface-roughness. Three intrinsic images are derived: reflectance, shading and meta- texture images. A constructive approach is proposed for computing a meta-texture image by preserving, equalizing and enhancing the underlying surface-roughness across color, brightness and illumination variations. We evaluate the performance on sample images and illustrate quantitatively that different patches of the same material, in an image, are normalized in their statistics despite variations in color, brightness and illumination. Finally, segmentation by line-based boundary-detection is proposed and results are provided and compared to known algorithms.},
	keywords = {analysis;image, colour, enhancement;image, image, image;salient, patches;image, segmentation;image, segmentation;meta-texture, surface-roughness;image, texture;},
	doi = {10.1109/ICCV.2007.4408930},
	author = {Yacoob,Yaser and Davis, Larry S.}
}
@conference {13654,
	title = {Superresolution-based enhancement of text in digital video},
	booktitle = {Pattern Recognition, 2000. Proceedings. 15th International Conference on},
	volume = {1},
	year = {2000},
	month = {2000///},
	pages = {847 -850 vol.1 - 847 -850 vol.1},
	abstract = {We present a superresolution-based text enhancement scheme to improve optical character recognition (OCR) and readability of text in digital video. The quality of video text is degraded by factors such as low resolution, antialiasing, clutter and blurring. We use the fact that the same text string often exists in consecutive frames to explore the temporal information and enhance the text image. For graphic text, we register text blocks to subpixel accuracy and fuse them to a new block with high resolution and a cleaner background. We use a projection onto convex sets based method to deblur scene text to improve readability. Experimental results show our scheme can improve OCR for graphic text and readability for scene text significantly},
	keywords = {accuracy;superresolution-based, antialiasing;blurring;clutter;digital, based, character, convex, Enhancement, enhancement;image, information;text, method;readability;subpixel, onto, recognition;, resolution;image, resolution;projection, scheme;temporal, sequences;optical, sets, string;video, text, text;geometry;image, text;low, video;graphic},
	doi = {10.1109/ICPR.2000.905546},
	author = {Huiping Li and David Doermann}
}
