We propose a novel technique for the registration of 3D point clouds which makes very few assumptions: we avoid any manual rough alignment or the use of landmarks, displacement can be arbitrarily large, and the two po...
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We introduce in this paper two probabilistic reasoning models (PRM-I and PRM-2) which combine the Principal Component Analysis (PCA) technique and the Bayes classifier and show their feasibility on the face recognitio...
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ISBN:
(纸本)0818684976
We introduce in this paper two probabilistic reasoning models (PRM-I and PRM-2) which combine the Principal Component Analysis (PCA) technique and the Bayes classifier and show their feasibility on the face recognition problem. The conditional probability density function for each class is modeled using the within class scatter and the Maximum A Posteriori (MAP) classification rule is implemented in the reduced PCA subspace. Experiments carried out using 1107 facial images corresponding to 369 subjects (with 169 subjects having duplicate images) from the FERET database show that the PRM approach compares favorably against the two well-known methods for face recognition the Eigenfaces and Fisherfaces.
We present a new framework for recognizing planar object classes, which is based on local feature detectors and a probabilistic model of the spatial arrangement of the features. The allowed object deformations are rep...
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ISBN:
(纸本)0818672587
We present a new framework for recognizing planar object classes, which is based on local feature detectors and a probabilistic model of the spatial arrangement of the features. The allowed object deformations are represented through shape statistics, which are learned from examples. Instances of an object in an image are detected by finding the appropriate features in the correct spatial configuration. The algorithm is robust with respect to partial occlusion, detector false alarms, and missed features. A 94% success rate was achieved for the problem of locating quasi-frontal views of faces in cluttered scenes.
In this paper we consider the problem of describing the action being performed by human figures in still images. We will attack this problem using an unsupervised learning approach, attempting to discover the set of a...
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We propose human action detection based on a successive convex matching scheme. Human actions are represented as sequences of postures and specific actions are detected in video by matching the time-coupled posture se...
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Nonnegative tensor factorization (NTF) is a recent multiway (multilinear) extension of nonnegative matrix factorization (NMF), where nonnegativity constraints are imposed on the CANDECOMP/PARAFAC model. In this paper ...
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ISBN:
(纸本)9781424411795
Nonnegative tensor factorization (NTF) is a recent multiway (multilinear) extension of nonnegative matrix factorization (NMF), where nonnegativity constraints are imposed on the CANDECOMP/PARAFAC model. In this paper we consider the Tucker model with nonnegativity constraints and develop a new tensor factorization method, referred to as nonnegative Tucker decomposition (NTD). The main contributions of this paper include: (1) multiplicative updating algorithms for NTD;(2) an initialization method for speeding up convergence;(3) a sparseness control method in tensor factorization. Through several computervision examples, we show the useful behavior of the NTD, over existing NTF and NMF methods.
Traditional stereo correspondence algorithms rely heavily on the Lambertian model of diffuse reflectance. While this diffuse assumption is generally valid for much of an image, processing of regions that contain specu...
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This paper describes a general methodology for automated recognition of complex human activities. The methodology uses a context-free grammar (CFG) based representation scheme to represent composite actions and intera...
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In this paper we present an efficient hierarchical approach to structure from motion for long image sequences. There are two key elements to our approach: accurate 3D reconstruction for each segment and efficient bund...
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In this paper we present an efficient hierarchical approach to structure from motion for long image sequences. There are two key elements to our approach: accurate 3D reconstruction for each segment and efficient bundle adjustment for the whole sequence. The image sequence is first divided into a number of segments so that feature points can be reliably tracked across each segment. Each segment has a long baseline to ensure accurate 3D reconstruction. To efficiently bundle adjust 3D structures from all segments, we reduce the number of frames in each segment by introducing `virtual key frames'. The virtual frames encode the 3D structure of each segment along with its uncertainty but they form a small subset of the original frames. Our method achieves significant speedup over conventional bundle adjustment methods.
With the growing interest in object categorization various methods have emerged that perform well in this challenging task, yet are inherently limited to only a moderate number of object classes. In pursuit of a more ...
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