@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 {12486,
	title = {Pose estimation in heavy clutter using a multi-flash camera},
	booktitle = {Robotics and Automation (ICRA), 2010 IEEE International Conference on},
	year = {2010},
	month = {2010/05//},
	pages = {2028 - 2035},
	abstract = {We propose a novel solution to object detection, localization and pose estimation with applications in robot vision. The proposed method is especially applicable when the objects of interest may not be richly textured and are immersed in heavy clutter. We show that a multi-flash camera (MFC) provides accurate separation of depth edges and texture edges in such scenes. Then, we reformulate the problem, as one of finding matches between the depth edges obtained in one or more MFC images to the rendered depth edges that are computed offline using 3D CAD model of the objects. In order to facilitate accurate matching of these binary depth edge maps, we introduce a novel cost function that respects both the position and the local orientation of each edge pixel. This cost function is significantly superior to traditional Chamfer cost and leads to accurate matching even in heavily cluttered scenes where traditional methods are unreliable. We present a sub-linear time algorithm to compute the cost function using techniques from 3D distance transforms and integral images. Finally, we also propose a multi-view based pose-refinement algorithm to improve the estimated pose. We implemented the algorithm on an industrial robot arm and obtained location and angular estimation accuracy of the order of 1 mm and 2 $\#$x00B0; respectively for a variety of parts with minimal texture.},
	keywords = {3D, algorithm;object, based, camera;multiview, depth, detection;object, detection;pose, distance, edge, edges;cameras;image, edges;integral, estimation;binary, estimation;multiflash, estimation;robot, function;depth, images;location, localization;pose, maps, matching;cost, matching;image, pose-refinement, texture;object, transforms;angular, vision;texture, vision;transforms;},
	doi = {10.1109/ROBOT.2010.5509897},
	author = {Ming-Yu Liu and Tuzel, O. and Veeraraghavan,A. and Chellapa, Rama and Agrawal,A. and Okuda, H.}
}
@conference {13978,
	title = {Canny edge detection on NVIDIA CUDA},
	booktitle = {Computer Vision and Pattern Recognition Workshops, 2008. CVPRW {\textquoteright}08. IEEE Computer Society Conference on},
	year = {2008},
	month = {2008/06//},
	pages = {1 - 8},
	abstract = {The Canny edge detector is a very popular and effective edge feature detector that is used as a pre-processing step in many computer vision algorithms. It is a multi-step detector which performs smoothing and filtering, non-maxima suppression, followed by a connected-component analysis stage to detect ldquotruerdquo edges, while suppressing ldquofalserdquo non edge filter responses. While there have been previous (partial) implementations of the Canny and other edge detectors on GPUs, they have been focussed on the old style GPGPU computing with programming using graphical application layers. Using the more programmer friendly CUDA framework, we are able to implement the entire Canny algorithm. Details are presented along with a comparison with CPU implementations. We also integrate our detector in to MATLAB, a popular interactive simulation package often used by researchers. The source code will be made available as open source.},
	keywords = {algorithms;connected-component, analysis, application, Canny, CUDA;computer, detection;feature, detection;NVIDIA, detector;filtering;graphical, detector;non, edge, extraction;smoothing, feature, filter, layers;multistep, methods;, responses;nonmaxima, stage;edge, suppression;smoothing;computer, VISION, vision;edge},
	doi = {10.1109/CVPRW.2008.4563088},
	author = {Luo,Yuancheng and Duraiswami, Ramani}
}
@article {18191,
	title = {Data hiding in curves with application to fingerprinting maps},
	journal = {Signal Processing, IEEE Transactions on},
	volume = {53},
	year = {2005},
	month = {2005/10//},
	pages = {3988 - 4005},
	abstract = {This paper presents a new data hiding method for curves. The proposed algorithm parameterizes a curve using the B-spline model and adds a spread spectrum sequence to the coordinates of the B-spline control points. In order to achieve robust fingerprint detection, an iterative alignment-minimization algorithm is proposed to perform curve registration and to deal with the nonuniqueness of B-spline control points. Through experiments, we demonstrate the robustness of the proposed data-hiding algorithm against various attacks, such as collusion, cropping, geometric transformations, vector/raster-raster/vector conversions, printing-and-scanning, and some of their combinations. We also show the feasibility of our method for fingerprinting topographic maps as well as writings and drawings.},
	keywords = {(mathematics);, algorithm;, alignment-minimization, B-spline, CONTROL, curve, data, detection;, edge, embedding;, encapsulation;, fingerprint, geospatial, hiding, identification;, image, iterative, method;, methods;, minimisation;, point;, protection;, registration;, sequence;, spectrum, splines, spread, watermarking;},
	isbn = {1053-587X},
	doi = {10.1109/TSP.2005.855411},
	author = {Gou,Hongmei and M. Wu}
}
@conference {13186,
	title = {Fast multiple object tracking via a hierarchical particle filter},
	booktitle = {Computer Vision, 2005. ICCV 2005. Tenth IEEE International Conference on},
	volume = {1},
	year = {2005},
	month = {2005/10//},
	pages = {212 - 219 Vol. 1 - 212 - 219 Vol. 1},
	abstract = {A very efficient and robust visual object tracking algorithm based on the particle filter is presented. The method characterizes the tracked objects using color and edge orientation histogram features. While the use of more features and samples can improve the robustness, the computational load required by the particle filter increases. To accelerate the algorithm while retaining robustness we adopt several enhancements in the algorithm. The first is the use of integral images for efficiently computing the color features and edge orientation histograms, which allows a large amount of particles and a better description of the targets. Next, the observation likelihood based on multiple features is computed in a coarse-to-fine manner, which allows the computation to quickly focus on the more promising regions. Quasi-random sampling of the particles allows the filter to achieve a higher convergence rate. The resulting tracking algorithm maintains multiple hypotheses and offers robustness against clutter or short period occlusions. Experimental results demonstrate the efficiency and effectiveness of the algorithm for single and multiple object tracking.},
	keywords = {(numerical, algorithm;, analysis;, Color, colour, Computer, Convergence, detection;, edge, fast, filter;, Filtering, hierarchical, histogram;, image, images;, integral, likelihood;, methods);, methods;, multiple, numerical, object, observation, of, orientation, particle, processes;, quasirandom, random, sampling;, tracking, tracking;, vision;, visual},
	doi = {10.1109/ICCV.2005.95},
	author = {Yang,Changjiang and Duraiswami, Ramani and Davis, Larry S.}
}
@conference {13335,
	title = {The half-edge tree: a compact data structure for level-of-detail tetrahedral meshes},
	booktitle = {Shape Modeling and Applications, 2005 International Conference},
	year = {2005},
	month = {2005/06//},
	pages = {332 - 337},
	abstract = {We propose a new data structure for the compact encoding of a level-of detail (LOD) model of a three-dimensional scalar field based on unstructured tetrahedral meshes. Such data structure, called a half-edge tree (HET), is built through the iterative application of a half-edge collapse, i.e. by contracting an edge to one of its endpoints. We also show that selective refined meshes extracted from an HET contain on average about 34\% and up to 75\% less tetrahedra than those extracted from an LOD model built through a general edge collapse.},
	keywords = {application;, compact, computational, data, detection;, edge, encoding;, generation;, geometry;, half-edge, iterative, level-of-detail, mesh, meshes;, methods;, model;, structure;, structures;, tetrahedral, tree, tree;},
	doi = {10.1109/SMI.2005.47},
	author = {Danovaro,E. and De Floriani, Leila and Magillo,P. and Puppo,E. and Sobrero,D. and Sokolovsky,N.}
}
@conference {13240,
	title = {Exemplar-based tracking and recognition of arm gestures},
	booktitle = {Image and Signal Processing and Analysis, 2003. ISPA 2003. Proceedings of the 3rd International Symposium on},
	volume = {2},
	year = {2003},
	month = {2003/09//},
	pages = {656 - 661 Vol.2 - 656 - 661 Vol.2},
	abstract = {This paper presents a probabilistic exemplar-based framework for recognizing gestures. The approach is based on representing each gesture as a sequence of learned body poses. The gestures are recognized through a probabilistic framework for matching these body poses and for imposing temporal constrains between different poses. Matching individual poses to image data is performed using a probabilistic formulation for edge matching to obtain a likelihood measurement for each individual pose. The paper introduces a correspondence-free weighted matching scheme for edge templates that emphasize discriminating features in the matching. The weighting does not require establishing correspondences between the different pose models. The probabilistic framework also imposes temporal constrains between different pose through a learned hidden Markov model (HMM) of each gesture.},
	keywords = {arm, constrains;, correspondence-free, edge, exemplar-based, framework;, gesture, hidden, HMM;, image, logic;, Markov, MATCHING, matching;, model;, models;, probabilistic, recognition;, scheme;, segmentation;, temporal, tracking;, weighted},
	doi = {10.1109/ISPA.2003.1296358},
	author = {Elgammal,A. and Shet,V. and Yacoob,Yaser and Davis, Larry S.}
}
@conference {13247,
	title = {Learning dynamics for exemplar-based gesture recognition},
	booktitle = {Computer Vision and Pattern Recognition, 2003. Proceedings. 2003 IEEE Computer Society Conference on},
	volume = {1},
	year = {2003},
	month = {2003/06//},
	pages = {I-571 - I-578 vol.1 - I-571 - I-578 vol.1},
	abstract = {This paper addresses the problem of capturing the dynamics for exemplar-based recognition systems. Traditional HMM provides a probabilistic tool to capture system dynamics and in exemplar paradigm, HMM states are typically coupled with the exemplars. Alternatively, we propose a non-parametric HMM approach that uses a discrete HMM with arbitrary states (decoupled from exemplars) to capture the dynamics over a large exemplar space where a nonparametric estimation approach is used to model the exemplar distribution. This reduces the need for lengthy and non-optimal training of the HMM observation model. We used the proposed approach for view-based recognition of gestures. The approach is based on representing each gesture as a sequence of learned body poses (exemplars). The gestures are recognized through a probabilistic framework for matching these body poses and for imposing temporal constraints between different poses using the proposed non-parametric HMM.},
	keywords = {arbitrary, body, by, Computer, constraint;, detection;, discrete, distribution, dynamics;, edge, estimation;, example;, exemplar, exemplar-based, extraction;, feature, framework;, gesture, gesture;, hidden, HMM;, human, image, learning, Markov, matching;, model;, models;, motion;, nonparametric, pose, probabilistic, recognition;, sequence;, space;, state;, statistics;, system, temporal, tool;, view-based, vision;},
	doi = {10.1109/CVPR.2003.1211405},
	author = {Elgammal,A. and Shet,V. and Yacoob,Yaser 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 {16914,
	title = {Content-based image retrieval using Fourier descriptors on a logo database},
	booktitle = {Pattern Recognition, 2002. Proceedings. 16th International Conference on},
	volume = {3},
	year = {2002},
	month = {2002///},
	pages = {521 - 524 vol.3 - 521 - 524 vol.3},
	abstract = {A system that enables the pictorial specification of queries in an image database is described. The queries are comprised of rectangle, polygon, ellipse, and B-spline shapes. The queries specify which shapes should appear in the target image as well as spatial constraints on the distance between them and their relative position. The retrieval process makes use of an abstraction of the contour of the shape which is invariant against translation, scale, rotation, and starting point, that is based on the use of Fourier descriptors. These abstractions are used in a system to locate logos in an image database. The utility of this approach is illustrated using some sample queries.},
	keywords = {abstraction;, analysis;, constraints;, content-based, contour, database, database;, databases;, descriptors;, detection;, edge, Fourier, image, logos;, pictorial, processing;, query, retrieval;, SHAPE, spatial, specification;, theory;, visual},
	doi = {10.1109/ICPR.2002.1047991},
	author = {Folkers,A. and Samet, Hanan}
}
@conference {13566,
	title = {Hidden loop recovery for handwriting recognition},
	booktitle = {Frontiers in Handwriting Recognition, 2002. Proceedings. Eighth International Workshop on},
	year = {2002},
	month = {2002///},
	pages = {375 - 380},
	abstract = {One significant challenge in the recognition of off-line handwriting is in the interpretation of loop structures. Although this information is readily available in online representation, close proximity of strokes often merges their centers making them difficult to identify. In this paper a novel approach to the recovery of hidden loops in off-line scanned document images is presented. The proposed algorithm seeks blobs that resemble truncated ellipses. We use a sophisticated form analysis method based on mutual distance measurements between the two sides of a symmetric shape. The experimental results are compared with the ground truth of the online representations of each off-line word image. More than 86\% percent of the meaningful loops are handled correctly.},
	keywords = {analysis;, character, contour, cursive, detection;, distance, edge, ellipses;, form, handwritten, hidden, loop, measurements;, mutual, partitioning;, recognition;, recovery;, SHAPE, shape;, sophisticated, strokes;, symmetric, truncated, word},
	doi = {10.1109/IWFHR.2002.1030939},
	author = {David Doermann and Intrator,N. and Rivin,E. and Steinherz,T.}
}
@article {12748,
	title = {Optimal edge-based shape detection},
	journal = {Image Processing, IEEE Transactions on},
	volume = {11},
	year = {2002},
	month = {2002/11//},
	pages = {1209 - 1227},
	abstract = {We propose an approach to accurately detecting two-dimensional (2-D) shapes. The cross section of the shape boundary is modeled as a step function. We first derive a one-dimensional (1-D) optimal step edge operator, which minimizes both the noise power and the mean squared error between the input and the filter output. This operator is found to be the derivative of the double exponential (DODE) function, originally derived by Ben-Arie and Rao (1994). We define an operator for shape detection by extending the DODE filter along the shape{\textquoteright}s boundary contour. The responses are accumulated at the centroid of the operator to estimate the likelihood of the presence of the given shape. This method of detecting a shape is in fact a natural extension of the task of edge detection at the pixel level to the problem of global contour detection. This simple filtering scheme also provides a tool for a systematic analysis of edge-based shape detection. We investigate how the error is propagated by the shape geometry. We have found that, under general assumptions, the operator is locally linear at the peak of the response. We compute the expected shape of the response and derive some of its statistical properties. This enables us to predict both its localization and detection performance and adjust its parameters according to imaging conditions and given performance specifications. Applications to the problem of vehicle detection in aerial images, human facial feature detection, and contour tracking in video are presented.},
	keywords = {1D, 2D, aerial, analysis;, boundary, conditions;, contour, cross, detection;, DODE, double, edge, edge-based, error, error;, exponential, extraction;, facial, feature, filter, filter;, Filtering, function;, geometry;, global, human, images;, imaging, localization, mean, methods;, NOISE, operator;, optimal, optimisation;, output;, performance;, pixel;, power;, propagation;, properties;, section;, SHAPE, square, squared, statistical, step, theory;, tracking;, two-dimensional, vehicle, video;},
	isbn = {1057-7149},
	doi = {10.1109/TIP.2002.800896},
	author = {Moon, H. and Chellapa, Rama and Rosenfeld, A.}
}
@conference {14855,
	title = {Stochastic completion fields: a neural model of illusory contour shape and salience},
	booktitle = {Computer Vision, 1995. Proceedings., Fifth International Conference on},
	year = {1995},
	month = {1995/06//},
	pages = {408 - 415},
	abstract = {We describe an algorithm and representation level theory of illusory contour shape and salience. Unlike previous theories, our model is derived from a single assumption-namely, that the prior probability distribution of boundary completion shape can be modeled by a random walk in a lattice whose points are positions and orientations in the image plane (i.e. the space which one can reasonably assume is represented by neurons of the mammalian visual cortex). Our model does not employ numerical relaxation or other explicit minimization, but instead relies on the fact that the probability that a particle following a random walk will pass through a given position and orientation on a path joining two boundary fragments can be computed directly as the product of two vector-field convolutions. We show that for the random walk we define, the maximum likelihood paths are curves of least energy, that is, on average, random walks follow paths commonly assumed to model the shape of illusory contours. A computer model is demonstrated on numerous illusory contour stimuli from the literature},
	keywords = {boundary, completion, computational, Computer, contour, contours;, convolutions;, cortex;, curves, detection;, distribution;, edge, energy;, estimation;, fields;, fragments;, geometry;, illusory, image, lattice;, least, likelihood, mammalian, maximum, model;, nets;, neural, of, paths;, plane;, probability, probability;, random, recognition;, shape;, stimuli;, Stochastic, vector-field, visual, walk;},
	doi = {10.1109/ICCV.1995.466910},
	author = {Williams,L. R and Jacobs, David W.}
}
