@conference {12608,
	title = {Shape-Regulated Particle Filtering for Tracking Non-Rigid Objects},
	booktitle = {Image Processing, 2006 IEEE International Conference on},
	year = {2006},
	month = {2006/10//},
	pages = {2813 - 2816},
	abstract = {This paper presents an active contour based algorithm for tracking non-rigid objects in heavily cluttered scenes. We decompose the non-rigid contour tracking problem into three subproblems: 2D motion estimation, deformation detection, and shape regulation. First, we employ a particle filter to estimate the affine transform parameters between successive frames. Second, by using a dynamic object model, we generate a probabilistic map of deformation to reshape its contour. Finally, we project the updated model onto a trained shape subspace to constrain deformations to be within possible object appearances. Our experiments show that the proposed algorithm significantly improves the performance of the tracker},
	keywords = {(numerical, 2D, algorithm;affine, based, contour, detection;dynamic, detection;motion, detection;parameter, estimation;active, estimation;object, estimation;particle, Filtering, filtering;probabilistic, filters;, map;shape-regulation;affine, methods);tracking, model;nonrigid, MOTION, object, scenes;deformation, tracking;parameter, transform;cluttered, transforms;clutter;edge},
	doi = {10.1109/ICIP.2006.312993},
	author = {Jie Shao and Chellapa, Rama and Porikli, F.}
}
@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.}
}
@article {11994,
	title = {Visual shape computation},
	journal = {Proceedings of the IEEE},
	volume = {76},
	year = {1988},
	month = {1988/08//},
	pages = {899 - 916},
	abstract = {Perceptual processes responsible for computing shape from several cues, including shading, texture, contour, and stereo, are examined. It is noted that these computational problems, as well as that of computing shaping from motion, are ill-posed in the sense of Hadamard. It is suggested that regularization theory can be used along with a priori knowledge to restrict the space of possible solutions, and thus restore the problem{\textquoteright}s well-prosedness. Some alternative methods are outlined, and the idea of active vision is explored briefly in connection with the problem},
	keywords = {a priori knowledge, active vision, computational problems, Computer vision, computing shaping from motion, contour, cues, Focusing, HUMANS, ill posed problems, Machine vision, Psychology, regularization theory, RETINA, sense of Hadamard, shading, SHAPE, space of possible solutions, Stereo vision, Surface texture, TEXTURE, visual shape computation, Visual system},
	isbn = {0018-9219},
	doi = {10.1109/5.5964},
	author = {Aloimonos, J.}
}
