@article {14886,
	title = {Lambertian reflectance and linear subspaces},
	journal = {Pattern Analysis and Machine Intelligence, IEEE Transactions on},
	volume = {25},
	year = {2003},
	month = {2003/02//},
	pages = {218 - 233},
	abstract = {We prove that the set of all Lambertian reflectance functions (the mapping from surface normals to intensities) obtained with arbitrary distant light sources lies close to a 9D linear subspace. This implies that, in general, the set of images of a convex Lambertian object obtained under a wide variety of lighting conditions can be approximated accurately by a low-dimensional linear subspace, explaining prior empirical results. We also provide a simple analytic characterization of this linear space. We obtain these results by representing lighting using spherical harmonics and describing the effects of Lambertian materials as the analog of a convolution. These results allow us to construct algorithms for object recognition based on linear methods as well as algorithms that use convex optimization to enforce nonnegative lighting functions. We also show a simple way to enforce nonnegative lighting when the images of an object lie near a 4D linear space. We apply these algorithms to perform face recognition by finding the 3D model that best matches a 2D query image.},
	keywords = {2D, 4D, 9D, analog;, analytic, characterization;, convex, convolution, distant, functions;, harmonics;, image, image;, intensities;, Lambertian, light, lighting, linear, methods;, nonnegative, normals;, object, optimization;, programming;, query, recognition;, reflectance;, reflectivity;, set;, sources;, space;, spherical, subspace;, subspaces;, surface},
	isbn = {0162-8828},
	doi = {10.1109/TPAMI.2003.1177153},
	author = {Basri,R. and Jacobs, David W.}
}
@conference {12724,
	title = {View synthesis of articulating humans using visual hull},
	booktitle = {Multimedia and Expo, 2003. ICME {\textquoteright}03. Proceedings. 2003 International Conference on},
	volume = {1},
	year = {2003},
	month = {2003/07//},
	pages = {I - 489-92 vol.1 - I - 489-92 vol.1},
	abstract = {In this paper, we present a method, which combines image-based visual hull and human body part segmentation for overcoming the inability of the visual hull method to reconstruct concave regions. The virtual silhouette image corresponding to the given viewing direction is first produced with image-based visual hull. Human body part localization technique is used to segment the input images and the rendered virtual silhouette image into convex body parts. The body parts in the virtual view are generated separately from the corresponding body parts in the input views and then assembled together. The previously rendered silhouette image is used to locate the corresponding body parts in input views and avoid the unconnected or squeezed regions in the assembled final view. Experiments show that this method can improve the reconstruction of concave regions for human postures and texture mapping.},
	keywords = {analysis;, body, convex, gesture, hull;, human, image, image-based, image;, mapping;, MOTION, part, parts;, postures;, recognition;, reconstruction;, segmentation;, silhouette, synthesis;, TEXTURE, texture;, view, virtual, visual},
	doi = {10.1109/ICME.2003.1220961},
	author = {Yue,Zhanfeng and Liang Zhao and Chellapa, Rama}
}
@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}
}
@article {14889,
	title = {Robust and efficient detection of salient convex groups},
	journal = {Pattern Analysis and Machine Intelligence, IEEE Transactions on},
	volume = {18},
	year = {1996},
	month = {1996/01//},
	pages = {23 - 37},
	abstract = {This paper describes an algorithm that robustly locates salient convex collections of line segments in an image. The algorithm is guaranteed to find all convex sets of line segments in which the length of the gaps between segments is smaller than some fixed proportion of the total length of the lines. This enables the algorithm to find convex groups whose contours are partially occluded or missing due to noise. We give an expected case analysis of the algorithm performance. This demonstrates that salient convexity is unlikely to occur at random, and hence is a strong clue that grouped line segments reflect underlying structure in the scene. We also show that our algorithm run time is O(n <sup>2</sup>log(n)+nm), when we wish to find the m most salient groups in an image with n line segments. We support this analysis with experiments on real data, and demonstrate the grouping system as part of a complete recognition system},
	keywords = {complexity;computer, complexity;contours;image, computational, convex, detection;feature, detection;object, extraction;object, groups;computational, organisation;proximity;salient, recognition;, recognition;line, recognition;perceptual, segment, vision;edge},
	isbn = {0162-8828},
	doi = {10.1109/34.476008},
	author = {Jacobs, David W.}
}
