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Connectionist Learning Procedures
 ARTIFICIAL INTELLIGENCE
, 1989
"... A major goal of research on networks of neuronlike processing units is to discover efficient learning procedures that allow these networks to construct complex internal representations of their environment. The learning procedures must be capable of modifying the connection strengths in such a way ..."
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Cited by 351 (6 self)
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A major goal of research on networks of neuronlike processing units is to discover efficient learning procedures that allow these networks to construct complex internal representations of their environment. The learning procedures must be capable of modifying the connection strengths in such a way that internal units which are not part of the input or output come to represent important features of the task domain. Several interesting gradientdescent procedures have recently been discovered. Each connection computes the derivative, with respect to the connection strength, of a global measure of the error in the performance of the network. The strength is then adjusted in the direction that decreases the error. These relatively simple, gradientdescent learning procedures work well for small tasks and the new challenge is to find ways of improving their convergence rate and their generalization abilities so that they can be applied to larger, more realistic tasks.
Modal Matching for Correspondence and Recognition
 IEEE Transactions on Pattern Analysis and Machine Intelligence
, 1995
"... Modal matching is a new method for establishing correspondences and computing canonical descriptions. The method is based on the idea of describing objects in terms of generalized symmetries, as defined by each object's eigenmodes. The resulting modal description is used for object recognition ..."
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Cited by 186 (7 self)
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Modal matching is a new method for establishing correspondences and computing canonical descriptions. The method is based on the idea of describing objects in terms of generalized symmetries, as defined by each object's eigenmodes. The resulting modal description is used for object recognition and categorization, where shape similarities are expressed as the amounts of modal deformation energy needed to align the two objects. In general, modes provide a globaltolocal ordering of shape deformation and thus allow for selecting which types of deformations are used in object alignment and comparison. In contrast to previous techniques, which required correspondence to be computed with an initial or prototype shape, modal matching utilizes a new type of finite element formulation that allows for an object's eigenmodes to be computed directly from available image information. This improved formulation provides greater generality and accuracy, and is applicable to data of any dimensionality. Correspondence results with 2D contour and point feature data are shown, and recognition experiments with 2D images of hand tools and airplanes are described.
Single Lens Stereo with a Plenoptic Camera
, 1992
"... Ordinary cameras gather light across the area of their lens aperture, and the light striking a given subregion of the aperture is structured somewhat differently than the light striking an adjacent subregion. By analyzing this optical structure, one can infer the depths of objects in the scene, i.e. ..."
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Cited by 124 (0 self)
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Ordinary cameras gather light across the area of their lens aperture, and the light striking a given subregion of the aperture is structured somewhat differently than the light striking an adjacent subregion. By analyzing this optical structure, one can infer the depths of objects in the scene, i.e., one can achieve "single lens stereo." We describe a novel camera for performing this analysis. It incorporates a single main lens along with a lenticular array placed at the sensor plane. The resulting "plenoptic camera" provides information about how the scene would look when viewed from a continuum of possible viewpoints bounded by the main lens aperture. Deriving depth information is simpler than in a binocular stereo system because the correspondence problem is minimized. The camera extracts information about both horizontal and vertical parallax, which improves the reliability of the depth estimates.
Using a Deformable Surface Model to Obtain a Shape Representation of the Cortex
 IEEE Trans. Med. Imag
, 1996
"... The problem of obtaining a mathematical representation of the cortex of the human brain is examined. A parametrization of the outer cortex is first obtained using a deformable surface algorithm which, motivated by the structure of the cortex, is constructed to find the central layer of thick surface ..."
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Cited by 93 (10 self)
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The problem of obtaining a mathematical representation of the cortex of the human brain is examined. A parametrization of the outer cortex is first obtained using a deformable surface algorithm which, motivated by the structure of the cortex, is constructed to find the central layer of thick surfaces. Based on this parametrization, a hierarchical representation of the cortical structure is proposed through its depth map and its curvature maps at various scales. Various experiments on magnetic resonance data are presented. I. Introduction The problem of finding and parametrizing boundaries in two and threedimensional images is often an important step toward shape visualization and analysis, and has been extensively studied in the image analysis and computer vision literature. Several methods have been proposed, basedboth on bottomup and topbottom procedures. One very promising model which combines robustness to noise and the flexibility to represent a broad class of shapes is base...
Cooperative Robust Estimation Using Layers of Support
 IEEE Transactions on Pattern Analysis and Machine Intelligence
, 1991
"... We present an approach to the problem of representing images that contain multiple objects or surfaces. Rather than use an edgebased approach to represent the segmentation of a scene, we propose a multilayer estimation framework which uses support maps to represent the segmentation of the image in ..."
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Cited by 88 (5 self)
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We present an approach to the problem of representing images that contain multiple objects or surfaces. Rather than use an edgebased approach to represent the segmentation of a scene, we propose a multilayer estimation framework which uses support maps to represent the segmentation of the image into homogeneous chunks. This supportbased approach can represent objects that are split into disjoint regions, or have surfaces that are transparently interleaved. Our framework is based on an extension of robust estimation methods which provide a theoretical basis for supportbased estimation. The Minimum Description Length principle is used to decide how many support maps to use in describing a particular image. We show results applying this framework to heterogeneous interpolation and segmentation tasks on range and motion imagery. 1 Introduction Realworld perceptual systems must deal with complicated and cluttered environments. To succeed in such environments, a system must be able to r...
An Active Contour Model For Mapping The Cortex
 IEEE TRANS. ON MEDICAL IMAGING
, 1995
"... A new active contour model for finding and mapping the outer cortex in brain images is developed. A crosssection of the brain cortex is modeled as a ribbon, and a constant speed mapping of its spine is sought. A variational formulation, an associated force balance condition, and a numerical approac ..."
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Cited by 67 (13 self)
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A new active contour model for finding and mapping the outer cortex in brain images is developed. A crosssection of the brain cortex is modeled as a ribbon, and a constant speed mapping of its spine is sought. A variational formulation, an associated force balance condition, and a numerical approach are proposed to achieve this goal. The primary difference between this formulation and that of snakes is in the specification of the external force acting on the active contour. A study of the uniqueness and fidelity of solutions is made through convexity and frequency domain analyses, and a criterion for selection of the regularization coefficient is developed. Examples demonstrating the performance of this method on simulated and real data are provided.
Reconstructing surfaces using anisotropic basis functions
 In International Conference on Computer Vision (ICCV) 2001
, 2001
"... Point sets obtained from computer vision techniques are often noisy and nonuniform. We present a new method of surface reconstruction that can handle such data sets using anisotropic basis functions. Our reconstruction algorithm draws upon the work in variational implicit surfaces for constructing ..."
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Cited by 52 (4 self)
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Point sets obtained from computer vision techniques are often noisy and nonuniform. We present a new method of surface reconstruction that can handle such data sets using anisotropic basis functions. Our reconstruction algorithm draws upon the work in variational implicit surfaces for constructing smooth and seamless 3D surfaces. Implicit functions are often formulated as a sum of weighted basis functions that are radially symmetric. Using radially symmetric basis functions inherently assumes, however, that the surface to be reconstructed is, everywhere, locally symmetric. Such an assumption is true only at planar regions, and hence, reconstruction using isotropic basis is insufficient to recover objects that exhibit sharp features. We preserve sharp features using anisotropic basis that allow the surface to vary locally. The reconstructed surface is sharper along edges and at corner points. We determine the direction of anisotropy at a point by performing principal component analysis of the data points in a small neighborhood. The resulting field of principle directions across the surface is smoothed through tensor filtering. We have applied the anisotropic basis functions to reconstruct surfaces from noisy synthetic 3D data and from real range data obtained from space carving. I.
Deformable Prototypes for Encoding Shape Categories in Image Databases
 PATTERN RECOGNITION, SPECIAL ISSUE ON IMAGE DATABASES
, 1997
"... We describe a method for shapebased image database search that uses deformable prototypes to represent categories. Rather than directly comparing a candidate shape with all shape entries in the database, shapes are compared in terms of the types of nonrigid deformations (differences) that relate th ..."
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Cited by 44 (2 self)
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We describe a method for shapebased image database search that uses deformable prototypes to represent categories. Rather than directly comparing a candidate shape with all shape entries in the database, shapes are compared in terms of the types of nonrigid deformations (differences) that relate them to a small subset of representative prototypes. To solve the shape correspondence and alignment problem, we employ the technique of modal matching, an informationpreserving shape decomposition for matching, describing, and comparing shapes despite sensor variations and nonrigid deformations. In modal matching, shape is decomposed into an ordered basis of orthogonal principal components. We demonstrate the utility of this approach for shape comparison in 2D image databases.
Reconstructing Surfaces By Volumetric Regularization Using Radial Basis Functions
"... We present a new method of surface reconstruction that generates smooth and seamless models from sparse, noisy, nonuniform, and low resolution range data. Data acquisition techniques from computer vision, such as stereo range images and space carving, produce 3D point sets that are imprecise and no ..."
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Cited by 38 (3 self)
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We present a new method of surface reconstruction that generates smooth and seamless models from sparse, noisy, nonuniform, and low resolution range data. Data acquisition techniques from computer vision, such as stereo range images and space carving, produce 3D point sets that are imprecise and nonuniform when compared to laser or optical range scanners. Traditional reconstruction algorithms designed for dense and precise data do not produce smooth reconstructions when applied to visionbased data sets. Our method constructs a 3D implicit surface, formulated as a sum of weighted radial basis functions. We achieve three primary advantages over existing algorithms: (1) the implicit functions we construct estimate the surface well in regions where there is little data; (2) the reconstructed surface is insensitive to noise in data acquisition because we can allow the surface to approximate, rather than exactly interpolate, the data; and (3) the reconstructed surface is locally detailed, yet globally smooth, because we use radial basis functions that achieve multiple orders of smoothness.
Automatic 3D reconstruction for face recognition
 In: Proc. Sixth IEEE Int. Conf. on Automatic Face and Gesture Recognition
, 2004
"... An analysisbysynthesis framework for face recognition with variant pose, illumination and expression (PIE) is proposed in this paper. First, an efficient 2Dto3D integrated face reconstruction approach is introduced to reconstruct a personalized 3D face model from a single frontal face image with ..."
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Cited by 26 (2 self)
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An analysisbysynthesis framework for face recognition with variant pose, illumination and expression (PIE) is proposed in this paper. First, an efficient 2Dto3D integrated face reconstruction approach is introduced to reconstruct a personalized 3D face model from a single frontal face image with neutral expression and normal illumination; Then, realistic virtual faces with different PIE are synthesized based on the personalized 3D face to characterize the face subspace; Finally, face recognition is conducted based on these representative virtual faces. Compared with other related works, this framework has the following advantages: 1) only one single frontal face is required for face recognition, which avoids the burdensome enrollment work; 2) the synthesized face samples provide the capability to conduct recognition under difficult conditions like complex PIE; and 3) the proposed 2Dto3D integrated face reconstruction approach is fully automatic and more efficient. The extensive experimental results show that the synthesized virtual faces significantly improve the accuracy of face recognition with variant PIE. 1.