We consider the problem of recognizing human actions from still images. We propose a novel approach that treats the pose of the person in the image as latent variables that will help with recognition. Different from o...
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ISBN:
(纸本)9781424469840
We consider the problem of recognizing human actions from still images. We propose a novel approach that treats the pose of the person in the image as latent variables that will help with recognition. Different from other work that learns separate systems for pose estimation and action recognition, then combines them in an ad-hoc fashion, our system is trained in an integrated fashion that jointly considers poses and actions. Our learning objective is designed to directly exploit the pose information for action recognition. Our experimental results demonstrate that by inferring the latent poses, we can improve the final action recognition results.
We develop methods to extract semantically meaningful symmetries from color images. These symmetries are defined within and between color hands using complex moments computed from the output of a bank of orientation a...
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ISBN:
(纸本)0780342364
We develop methods to extract semantically meaningful symmetries from color images. These symmetries are defined within and between color hands using complex moments computed from the output of a bank of orientation and scale selective filters. From this representation, we derive a set of features which are invariant to rotation, scale, and illumination a conditions. Experimental results are provided to show the performance of this set of features for classification and image database partitioning.
Winder et al. [15, 14] have recently shown the superiority of the DAISY descriptor [12] in comparison to other widely extended descriptors such as SIFT [8] and SURF [1]. Motivated by those results, we present a novel ...
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ISBN:
(纸本)9781424469840
Winder et al. [15, 14] have recently shown the superiority of the DAISY descriptor [12] in comparison to other widely extended descriptors such as SIFT [8] and SURF [1]. Motivated by those results, we present a novel algorithm that extracts viewpoint and illumination invariant keypoints and describes them with a particular implementation of a DAISY-like layout. We demonstrate how to efficiently compute the scale-space and re-use this information for the descriptor. Comparison to similar approaches such as SIFT and SURF show higher precision vs recall performance of the proposed method. Moreover, we dramatically reduce the computational cost by a factor of 6x and 3x, respectively. We also prove the use of the proposed method for computervision applications.
This paper presents a robust technique to detect local deteriorations of old cinematographic films. This method relies on spatio-temporal information and combines two different detectors : a morphological detector whi...
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ISBN:
(纸本)0780342364
This paper presents a robust technique to detect local deteriorations of old cinematographic films. This method relies on spatio-temporal information and combines two different detectors : a morphological detector which uses spatial properties of deteriorations, and a dynamic detector based on motion estimation techniques. Our deterioration detector has been validated Olt several film sequences and turned out to be a powerful tool for digital film restoration.
A local parallel method is described for computing the stochastic completion field introduced in an earlier report. The local parallel method can be interpreted as a stable finite difference scheme for solving the und...
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ISBN:
(纸本)0818672587
A local parallel method is described for computing the stochastic completion field introduced in an earlier report. The local parallel method can be interpreted as a stable finite difference scheme for solving the underlying Fokker-Planck equation identified by Mumford. The new method is more plausible as a neural model since (1) unlike the previous method, it can be computed in a sparse, locally connected network;and (2) the network dynamics are consistent with psycophysical measurements of the time course of illusory contour formation.
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.
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.
Previous work [5], [2] have developed an approach for estimating shape and albedo from multiple images assuming Lambertian reflectance with single light sources. The main contributions of this paper are: (i) to show h...
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ISBN:
(纸本)0780342364
Previous work [5], [2] have developed an approach for estimating shape and albedo from multiple images assuming Lambertian reflectance with single light sources. The main contributions of this paper are: (i) to show how the approach can be generalized to include ambient background illumination, (ii) to demonstrate the use of the integrability constraint for solving this problem, and (iii) an iterative algorithm which is able to improve the analysis by finding shadows and rejecting them.
Current methods for registering image regions perform well for simple transformations or large image regions. In this paper, we present a new method that is better able to handle small image regions as they deform wit...
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ISBN:
(纸本)0780342364
Current methods for registering image regions perform well for simple transformations or large image regions. In this paper, we present a new method that is better able to handle small image regions as they deform with non-linear transformations. We introduce difference decompositon, a novel approach to solving the registration problem. The method is a generalization of previous methods and can better handle non-linear transforms. Although the methods are general, we focus on projective transformations and introduce piecewise-projective transformations for modeling the motions of non-planar objects. We conclude with examples from our prototype implementation.
We propose an approach to find and describe objects within broad domains. We introduce a new dataset that provides annotation for sharing models of appearance and correlation across categories. We use it to learn part...
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ISBN:
(纸本)9781424469840
We propose an approach to find and describe objects within broad domains. We introduce a new dataset that provides annotation for sharing models of appearance and correlation across categories. We use it to learn part and category detectors. These serve as the visual basis for an integrated model of objects. We describe objects by the spatial arrangement of their attributes and the interactions between them. Using this model, our system can find animals and vehicles that it has not seen and infer attributes, such as function and pose. Our experiments demonstrate that we can more reliably locate and describe both familiar and unfamiliar objects, compared to a baseline that relies purely on basic category detectors.
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