@conference {13070,
	title = {A novel feature descriptor based on the shearlet transform},
	booktitle = {Image Processing (ICIP), 2011 18th IEEE International Conference on},
	year = {2011},
	month = {2011/09//},
	pages = {1033 - 1036},
	abstract = {Problems such as image classification, object detection and recognition rely on low-level feature descriptors to represent visual information. Several feature extraction methods have been proposed, including the Histograms of Oriented Gradients (HOG), which captures edge information by analyzing the distribution of intensity gradients and their directions. In addition to directions, the analysis of edge at different scales provides valuable information. Shearlet transforms provide a general framework for analyzing and representing data with anisotropic information at multiple scales. As a consequence, signal singularities, such as edges, can be precisely detected and located in images. Based on the idea of employing histograms to estimate the distribution of edge orientations and on the accurate multi-scale analysis provided by shearlet transforms, we propose a feature descriptor called Histograms of Shearlet Coefficients (HSC). Experimental results comparing HOG with HSC show that HSC provides significantly better results for the problems of texture classification and face identification.},
	keywords = {analysis;multiscale, analysis;object, classification;face, classification;image, classification;intensity, coefficients;image, descriptor;feature, detection;object, distribution, edge, EXTRACTION, extraction;image, gradient, gradients;histograms, identification;feature, methods;histograms, of, orientations;face, oriented, recognition;feature, recognition;shearlet, recognition;transforms;, shearlet, singularities;texture, texture;object, transform;signal},
	doi = {10.1109/ICIP.2011.6115600},
	author = {Schwartz, W.R. and da Silva,R.D. and Davis, Larry S. and Pedrini,H.}
}
@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}
}
@conference {12650,
	title = {An algebraic approach to surface reconstruction from gradient fields},
	booktitle = {Computer Vision, 2005. ICCV 2005. Tenth IEEE International Conference on},
	volume = {1},
	year = {2005},
	month = {2005/10//},
	pages = {174 - 181 Vol. 1 - 174 - 181 Vol. 1},
	abstract = {Several important problems in computer vision such as shape from shading (SFS) and photometric stereo (PS) require reconstructing a surface from an estimated gradient field, which is usually non-integrable, i.e. have non-zero curl. We propose a purely algebraic approach to enforce integrability in discrete domain. We first show that enforcing integrability can be formulated as solving a single linear system Ax =b over the image. In general, this system is under-determined. We show conditions under which the system can be solved and a method to get to those conditions based on graph theory. The proposed approach is non-iterative, has the important property of local error confinement and can be applied to several problems. Results on SFS and PS demonstrate the applicability of our method.},
	keywords = {algebra;, algebraic, approach;, Computer, confinement;, discrete, domain, error, field;, from, gradient, graph, image, integrability;, linear, local, methods;, photometric, reconstruction;, shading;, SHAPE, stereo;, surface, system;, theory;, vision;},
	doi = {10.1109/ICCV.2005.31},
	author = {Agrawal,A. and Chellapa, Rama and Raskar, R.}
}
@conference {13188,
	title = {Detecting rotational symmetries},
	booktitle = {Computer Vision, 2005. ICCV 2005. Tenth IEEE International Conference on},
	volume = {2},
	year = {2005},
	month = {2005/10//},
	pages = {954 - 961 Vol. 2 - 954 - 961 Vol. 2},
	abstract = {We present an algorithm for detecting multiple rotational symmetries in natural images. Given an image, its gradient magnitude field is computed, and information from the gradients is spread using a diffusion process in the form of a gradient vector flow (GVF) field. We construct a graph whose nodes correspond to pixels in tire image, connecting points that are likely to be rotated versions of one another The n-cycles present in tire graph are made to vote for C<sub>n</sub> symmetries, their votes being weighted by the errors in transformation between GVF in the neighborhood of the voting points, and the irregularity of the n-sided polygons formed by the voters. The votes are accumulated at tire centroids of possible rotational symmetries, generating a confidence map for each order of symmetry. We tested the method with several natural images.},
	keywords = {axial, computational, detection;, field;, flow;, geometry;, gradient, graph, graph;, image, image;, magnitude, methods;, multiple, n-sided, object, polygons;, recognition;, rotational, symmetries;, symmetry;, theory;, tire, tyres;, vector},
	doi = {10.1109/ICCV.2005.71},
	author = {Shiv Naga Prasad,V. and Davis, Larry S.}
}
@conference {13272,
	title = {A Smale-like decomposition for discrete scalar fields},
	booktitle = {Pattern Recognition, 2002. Proceedings. 16th International Conference on},
	volume = {1},
	year = {2002},
	month = {2002///},
	pages = {184 - 187 vol.1 - 184 - 187 vol.1},
	abstract = {In this paper we address the problem of representing the structure of the topology of a d-dimensional scalar field as a basis for constructing a multiresolution representation of the structure of such afield. To this aim, we define a discrete decomposition of a triangulated d-dimensional domain, on whose vertices the values of the field are given. We extend a Smale decomposition, defined by Thom (1949) and Smale (1960) for differentiable functions, to the discrete case, to what we call a Smale-like decomposition. We introduce the notion of discrete gradient vector field, which indicates the growth of the scalar field and matches with our decomposition. We sketch an algorithm for building a Smale-like decomposition and a graph-based representation of this decomposition. We present results for the case of two-dimensional fields.},
	keywords = {data, decomposition;, differentiable, discrete, domain;, field;, fields;, functions;, gradient, graph-based, methods;, multidimensional, multiresolution, representation;, scalar, Smale-like, structure, Topology, triangulated, vector, visualisation;},
	doi = {10.1109/ICPR.2002.1044644},
	author = {De Floriani, Leila and Mesmoudi,M. M. and Danovaro,E.}
}
