It is often necessary to handle randomness and geometry is computervision, for instance to match and fuse together noisy geometric features such as points, lines or 3D frames, or to estimate a geometric transformatio...
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ISBN:
(纸本)0818672587
It is often necessary to handle randomness and geometry is computervision, for instance to match and fuse together noisy geometric features such as points, lines or 3D frames, or to estimate a geometric transformation from a set of matched features. However, the proper handling of these geometric features is far more difficult than for points, and a number of paradoxes can arise. We analyse in this article three basic problems: (1) what is a uniform random distribution of features, (2) how to define a distance between features, and (3) what is the 'mean feature' of a number of feature measurements, and we propose generic methods to solve them.
Learning models for recognizing objects with few or no training examples is important, due to the intrinsic long-tailed distribution of objects in the real world. In this paper, we propose an approach to use comparati...
ISBN:
(纸本)9781424469840
Learning models for recognizing objects with few or no training examples is important, due to the intrinsic long-tailed distribution of objects in the real world. In this paper, we propose an approach to use comparative object similarity. The key insight is that: given a set of object categories which are similar and a set of categories which are dissimilar, a good object model should respond more strongly to examples from similar categories than to examples from dissimilar categories. We develop a regularized kernel machine algorithm to use this category dependent similarity regularization. Our experiments on hundreds of categories show that our method can make significant improvement, especially for categories with no examples.
The Perseus system is a purposive visual architecture that has been used to recognize the pointing gesture. recognition of this gesture is an important part of natural human-machine interfaces. Perseus is modularized ...
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ISBN:
(纸本)0818672587
The Perseus system is a purposive visual architecture that has been used to recognize the pointing gesture. recognition of this gesture is an important part of natural human-machine interfaces. Perseus is modularized into 6 types of components: feature maps, object representations, markers, visual routines, a segmentation map, and a long term visual memory. This structure not only allows Perseus to use knowledge about the task and environment at every stage of processing to more efficiently and accurately solve the pointing task, but also allows it to be extended to tasks other than recognizing pointing.
A systematic methodology is presented for automatic selection of scale levels when detecting one-dimensional features, such as edges and ridges. A novel concept of a scale-space edge is introduced and defined as a con...
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ISBN:
(纸本)0818672587
A systematic methodology is presented for automatic selection of scale levels when detecting one-dimensional features, such as edges and ridges. A novel concept of a scale-space edge is introduced and defined as a connected set of points in scale-space. Two specific measures of edge strength are analyzed in detail. It is shown that by expressing these in terms of γ-normalized derivatives, an immediate consequence of this definition is that fine scales are selected for sharp edges, whereas coarse scales are selected for diffuse edge, such that an edge model constitutes a valid abstraction of the intensity profile across the edge.
Many computervision and patternrecognition problems may be posed by defining a way of measuring dissimilarities between patterns. For many types of data, these dissimilarities are not Euclidean, and may not be metri...
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ISBN:
(纸本)9781424469840
Many computervision and patternrecognition problems may be posed by defining a way of measuring dissimilarities between patterns. For many types of data, these dissimilarities are not Euclidean, and may not be metric. In this paper, we provide a means of embedding such data. We aim to embed the data on a hypersphere whose radius of curvature is determined by the dissimilarity data. The hypersphere can be either of positive curvature (elliptic) or of negative curvature (hyperbolic). We give an efficient method for solving the elliptic and hyperbolic embedding problems on symmetric dissimilarity data. This method gives the radius of curvature and a method for approximating the objects as points on a hyperspherical manifold. We apply our method to a variety of data including shape-similarities, graph-similarity and gesture-similarity data. In each case the embedding maintains the local structure of the data while placing the points in a metric space.
We present a framework that retains ambiguity in feature matching to increase the performance of 3D object recognition systems. Whereas previous systems removed ambiguous correspondences during matching, we show that ...
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ISBN:
(纸本)9781424469840
We present a framework that retains ambiguity in feature matching to increase the performance of 3D object recognition systems. Whereas previous systems removed ambiguous correspondences during matching, we show that ambiguity should be resolved during hypothesis testing and not at the matching phase. To preserve ambiguity during matching, we vector quantize and match model features in a hierarchical manner. This matching technique allows our system to be more robust to the distribution of model descriptors in feature space. We also show that we can address recognition under arbitrary viewpoint by using our framework to facilitate matching of additional features extracted from affine transformed model images. The evaluation of our algorithms in 3D object recognition is demonstrated on a difficult dataset of 620 images.
pattern matching is a widely used procedure in signal processing, computervision, image and video processing. Recently, methods using Walsh Hadamard Transform (WHT) and Gray-Code kernels (GCK) are successfully applie...
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ISBN:
(纸本)9781424469840
pattern matching is a widely used procedure in signal processing, computervision, image and video processing. Recently, methods using Walsh Hadamard Transform (WHT) and Gray-Code kernels (GCK) are successfully applied for fast transform domain pattern matching. This paper introduces strip sum on the image. The sum of pixels in a rectangle can be computed by one addition using the strip sum. Then we propose to use the orthogonal Haar transform (OHT) for pattern matching. Applied for pattern matching, the algorithm using strip sum requires O(log u) additions per pixel to project input data of size N x N onto u 2-D OHT basis while existing fast algorithms require O(u) additions per pixel to project the same data onto u 2-D WHT or GCK basis. Experimental results show the efficiency of pattern matching using OHT.
recognition ambiguity, due to noisy measurements and uncertain object models, can be quantified and actively used by an autonomous agent to efficiently gather new data and improve its information about the environment...
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ISBN:
(纸本)0818672587
recognition ambiguity, due to noisy measurements and uncertain object models, can be quantified and actively used by an autonomous agent to efficiently gather new data and improve its information about the environment. In this work an information-based utility measure is used to derive from a learned classification of shape models an efficient data collection strategy, specifically aimed at increasing classification confidence when recognizing uncertain shapes. Promising simulation results are presented and discussed.
The basic limitations of the current appearance-based matching methods using eigenimages are non-robust estimation of coefficients and inability to cope with problems related to occlusions and segmentation. In this pa...
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ISBN:
(纸本)0818672587
The basic limitations of the current appearance-based matching methods using eigenimages are non-robust estimation of coefficients and inability to cope with problems related to occlusions and segmentation. In this paper we present a new approach which successfully solves these problems. The major novelty of our approach lies in the way how the coefficients of the eigenimages are determined. Instead of computing the coefficients by a projection of the data onto the eigenimages, we extract them by a hypothesize-and-test paradigm using subsets of image points. Competing hypotheses are then subject to a selection procedure based on the Minimum Description Length principle. The approach enables us not only to reject outliers and to deal with occlusions but also to simultaneously use multiple classes of eigenimages.
Lately, Multilinear Principal Component Analysis (MPCA) has been successfully applied to face recognition since MPCA provides analysis of multiple factors of face images such as people's identities, viewpoints, an...
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ISBN:
(纸本)9781424469840
Lately, Multilinear Principal Component Analysis (MPCA) has been successfully applied to face recognition since MPCA provides analysis of multiple factors of face images such as people's identities, viewpoints, and lighting conditions. MPCA employees multiple linear subspaces constructed by varying factors. In this paper, we propose nonlinear submanifold analysis, which can represent the variation of each factor more accurately than the conventional multilinear subspace analysis. Based on submanifold learning, we propose an extension of the multiple factor analysis. This paper proposes the kernel-based extension of MPCA whose definition of a kernel function and neighbors of each sample is robust for submanifold learning. The experimental results in this paper demonstrate that the proposed methods produce a synergetic advantage for face recognition. This is because our method offers the combined virtues of both multifactor analysis and manifold learning.
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