@article {14868,
	title = {Illumination Recovery From Image With Cast Shadows Via Sparse Representation},
	journal = {Image Processing, IEEE Transactions on},
	volume = {20},
	year = {2011},
	month = {2011/08//},
	pages = {2366 - 2377},
	abstract = {In this paper, we propose using sparse representation for recovering the illumination of a scene from a single image with cast shadows, given the geometry of the scene. The images with cast shadows can be quite complex and, therefore, cannot be well approximated by low-dimensional linear subspaces. However, it can be shown that the set of images produced by a Lambertian scene with cast shadows can be efficiently represented by a sparse set of images generated by directional light sources. We first model an image with cast shadows composed of a diffusive part (without cast shadows) and a residual part that captures cast shadows. Then, we express the problem in an <i> $\#$x2113;</i><sub>1</sub>-regularized least-squares formulation, with nonnegativity constraints (as light has to be non-negative at any point in space). This sparse representation enjoys an effective and fast solution thanks to recent advances in compressive sensing. In experiments on synthetic and real data, our approach performs favorably in comparison with several previously proposed methods.},
	keywords = {$\#$x2113;<sub>1</sub>-regularized, approximations;, coding;image, compression;image, constraints;sparse, formulation;Lambertian, illumination, image, least-square, light, linear, reconstruction;image, recovery;low-dimensional, representation;data, representation;least, scene;cast, sensing;directional, shadows;compressive, sources;image, squares, subspaces;nonnegativity},
	isbn = {1057-7149},
	doi = {10.1109/TIP.2011.2118222},
	author = {Mei,Xue and Ling,Haibin and Jacobs, David W.}
}
@conference {12511,
	title = {Enhancing sparsity using gradients for compressive sensing},
	booktitle = {Image Processing (ICIP), 2009 16th IEEE International Conference on},
	year = {2009},
	month = {2009/11//},
	pages = {3033 - 3036},
	abstract = {In this paper, we propose a reconstruction method that recovers images assumed to have a sparse representation in a gradient domain by using partial measurement samples that are collected in the Fourier domain. A key improvement of this technique is that it makes use of a robust generalized Poisson solver that greatly aids in achieving a significantly improved performance over similar proposed methods. Experiments provided also demonstrate that this new technique is more flexible to work with either random or restricted sampling scenarios better than its competitors.},
	keywords = {analysis;gradient, domain;compressive, domain;image, Fourier, generalized, measurement, methods;, methods;image, Poisson, reconstruction;image, reconstruction;partial, representation;Fourier, representation;sampling, samples;robust, scenarios;sparse, sensing;enhancing, solver;sampling, sparsity;gradient},
	doi = {10.1109/ICIP.2009.5414411},
	author = {Patel, Vishal M. and Easley,G. R and Chellapa, Rama and Healy,D. M}
}
@conference {12546,
	title = {Stochastic fusion of multi-view gradient fields},
	booktitle = {Image Processing, 2008. ICIP 2008. 15th IEEE International Conference on},
	year = {2008},
	month = {2008/10//},
	pages = {1324 - 1327},
	abstract = {Image gradients form powerful cues in a host of vision and graphics applications. In this paper, we consider multiple views of a textured planar scene and consider the problem of estimating the scene texture map using these multi-view inputs. Modeling each camera view as a projective transformation of the scene, we show that the problem is equivalent to that of studying the effect of noise (and the projective imaging) on the gradient fields induced by this texture map. We show that these noisy gradient fields can be modeled as complete observers of the scene radiance. Further, the corrupting noise can be shown to be additive and linear, although spatially varying. However, the specific form of the noise term can be exploited to design linear estimators that fuse the gradient fields obtained from each of the individual views. The fused gradient field forms a robust estimate of the scene gradients and can be used for scene reconstruction.},
	keywords = {application;brightness;cameras;gradient, application;image, camera, estimators;multiview, fields;projective, fusion;image, fusion;textured, gradient, gradients;linear, gradients;scene, imaging;scene, map;stochastic, methods;image, noise;graphics, planar, radiance;scene, reconstruction;image, reconstruction;scene, scene;vision, TEXTURE, texture;, view;corrupting},
	doi = {10.1109/ICIP.2008.4712007},
	author = {Sankaranarayanan,A. C and Chellapa, Rama}
}
@article {12540,
	title = {Synthesis of Silhouettes and Visual Hull Reconstruction for Articulated Humans},
	journal = {Multimedia, IEEE Transactions on},
	volume = {10},
	year = {2008},
	month = {2008/12//},
	pages = {1565 - 1577},
	abstract = {In this paper, we propose a complete framework for improved synthesis and understanding of the human pose from a limited number of silhouette images. It combines the active image-based visual hull (IBVH) algorithm and a contour-based body part segmentation technique. We derive a simple, approximate algorithm to decide the extrinsic parameters of a virtual camera, and synthesize the turntable image collection of the person using the IBVH algorithm by actively moving the virtual camera on a properly computed circular trajectory around the person. Using the turning function distance as the silhouette similarity measurement, this approach can be used to generate the desired pose-normalized images for recognition applications. In order to overcome the inability of the visual hull (VH) method to reconstruct concave regions, we propose a contour-based human body part localization algorithm to segment the silhouette images into convex body parts. The body parts observed from the virtual view are generated separately from the corresponding body parts observed from the input views and then assembled together for a more accurate VH reconstruction. Furthermore, the obtained turntable image collection helps to improve the body part segmentation and identification process. By using the inner distance shape context (IDSC) measurement, we are able to estimate the body part locations more accurately from a synthesized view where we can localize the body part more precisely. Experiments show that the proposed algorithm can greatly improve body part segmentation and hence shape reconstruction results.},
	keywords = {active, algorithm;articulated, algorithm;inner, body, camera;visual, collection;virtual, computation;contour-based, Context, detection;image, distance, distance;turntable, estimation;shape, function, hull, human, image, image;approximate, localization, measurement;silhouette, measurement;turning, part, pose;circular, reality;, recognition;virtual, reconstruction;approximation, reconstruction;image, segmentation, segmentation;pose, SHAPE, similarity, synthesis;silhouette, technique;human, theory;cameras;edge, Trajectory},
	isbn = {1520-9210},
	doi = {10.1109/TMM.2008.2007321},
	author = {Yue,Zhanfeng and Chellapa, Rama}
}
@article {12586,
	title = {Super-Resolution of Face Images Using Kernel PCA-Based Prior},
	journal = {Multimedia, IEEE Transactions on},
	volume = {9},
	year = {2007},
	month = {2007/06//},
	pages = {888 - 892},
	abstract = {We present a learning-based method to super-resolve face images using a kernel principal component analysis-based prior model. A prior probability is formulated based on the energy lying outside the span of principal components identified in a higher-dimensional feature space. This is used to regularize the reconstruction of the high-resolution image. We demonstrate with experiments that including higher-order correlations results in significant improvements},
	keywords = {analysis;learning-based, analysis;probability;, component, Face, image, method;prior, model;face, principal, probability, recognition;image, reconstruction;image, reconstruction;kernel, resolution;principal, super-resolution;high-resolution},
	isbn = {1520-9210},
	doi = {10.1109/TMM.2007.893346},
	author = {Chakrabarti,Ayan and Rajagopalan, AN and Chellapa, Rama}
}
@conference {12611,
	title = {Invariant Geometric Representation of 3D Point Clouds for Registration and Matching},
	booktitle = {Image Processing, 2006 IEEE International Conference on},
	year = {2006},
	month = {2006/10//},
	pages = {1209 - 1212},
	abstract = {Though implicit representations of surfaces have often been used for various computer graphics tasks like modeling and morphing of objects, it has rarely been used for registration and matching of 3D point clouds. Unlike in graphics, where the goal is precise reconstruction, we use isosurfaces to derive a smooth and approximate representation of the underlying point cloud which helps in generalization. Implicit surfaces are generated using a variational interpolation technique. Implicit function values on a set of concentric spheres around the 3D point cloud of object are used as features for matching. Geometric-invariance is achieved by decomposing implicit values based feature set into various spherical harmonics. The decomposition provides a compact representation of 3D point clouds while achieving rotation invariance},
	keywords = {3D, cloud;computer, function, geometric, graphics;geophysical, graphics;image, Interpolation, matching;image, point, processing;image, reconstruction;image, registration;image, registration;implicit, representation;interpolation;, representation;variational, signal, technique;clouds;computer, value;invariant},
	doi = {10.1109/ICIP.2006.312542},
	author = {Biswas,S. and Aggarwal,G. and Chellapa, Rama}
}
@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.}
}
