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.
Two challenges in computervision are to accommodate noisy data and missing data. Many problems in computervision, such as segmentation, filtering, stereo, reconstruction, inpainting and optical flow seek solutions t...
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ISBN:
(纸本)9781424469840
Two challenges in computervision are to accommodate noisy data and missing data. Many problems in computervision, such as segmentation, filtering, stereo, reconstruction, inpainting and optical flow seek solutions that match the data while satisfying an additional regularization, such as total variation or boundary length. A regularization which has received less attention is to minimize the curvature of the solution. One reason why this regularization has received less attention is due to the difficulty in finding an optimal solution to this image model, since many existing methods are complicated, slow and/or provide a suboptimal solution. Following the recent progress of Schoenemann et al. [28], we provide a simple formulation of curvature regularization which admits a fast optimization which gives globally optimal solutions in practice. We demonstrate the effectiveness of this method by applying this curvature regularization to image segmentation.
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.
We present a new method for synthesizing novel views of a 3D scene from few model images in full correspondence. The core of this work is the derivation of a tensorial operator that describes the transformation from) ...
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ISBN:
(纸本)0780342364
We present a new method for synthesizing novel views of a 3D scene from few model images in full correspondence. The core of this work is the derivation of a tensorial operator that describes the transformation from) a given tensor of three views to a novel tensor of a new configuration of three views. BL repeated application of the operator an a seed tensor with a sequence of desired virtual camera positions we obtain a chain of warping functions (tensors) from the set of model images to create the desired virtual views.
We consider the problem of learning to map between two vector spaces given pairs of matching vectors, one from each space. This problem naturally arises in numerous vision problems, for example, when mapping between t...
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ISBN:
(纸本)9781424469840
We consider the problem of learning to map between two vector spaces given pairs of matching vectors, one from each space. This problem naturally arises in numerous vision problems, for example, when mapping between the images of two cameras, or when the annotations of each image is multidimensional. We focus on the common asymmetric case, where one vector space X is more informative than the other Y, and find a transformation from Y to X. We present a new optimization problem that aims to replicate in the transformed Y the margins that dominate the structure of X. This optimization problem is convex, and efficient algorithms are presented. Links to various existing methods such as CCA and SVM are drawn, and the effectiveness of the method is demonstrated in several visual domains.
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.
We derive a sensitivity analysis for moment invariants of multidimensional distributions, These invariants have many uses in computational systems and have recently been used for illumination-invariant recognition in ...
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ISBN:
(纸本)0780342364
We derive a sensitivity analysis for moment invariants of multidimensional distributions, These invariants have many uses in computational systems and have recently been used for illumination-invariant recognition in color images. In this context, the sensitivity analysis predicts the response of moment invariants to partial occlusion. Using the results of the sensitivity analysis, we develop a novel surface representation called the invariant profile which captures color distribution and spatial information while remaining invariant to the spectral content of the scene illumination. Unlike previous representations, the recognition of invariant profiles does not require illumination correction. We demonstrate the sensitivity analysis and the use of invariant profiles for recognition with a set of experiments on color images.
Self-similarity is an attractive image property which has recently found its way into object recognition in the form of local self-similarity descriptors [5, 6, 14, 18, 23, 27] In this paper we explore global self-sim...
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ISBN:
(纸本)9781424469840
Self-similarity is an attractive image property which has recently found its way into object recognition in the form of local self-similarity descriptors [5, 6, 14, 18, 23, 27] In this paper we explore global self-similarity (GSS) and its advantages over local self-similarity (LSS). We make three contributions: (a) we propose computationally efficient algorithms to extract GSS descriptors for classification. These capture the spatial arrangements of self-similarities within the entire image;(b) we show how to use these descriptors efficiently for detection in a sliding-window framework and in a branch-and-bound framework;(c) we experimentally demonstrate on Pascal VOC 2007 and on ETHZ Shape Classes that GSS outperforms LSS for both classification and detection, and that GSS descriptors are complementary to conventional descriptors such as gradients or color.
We are interested in identifying the material category, e.g. glass, metal, fabric, plastic or wood, from a single image of a surface. Unlike other visual recognition tasks in computervision, it is difficult to find g...
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ISBN:
(纸本)9781424469840
We are interested in identifying the material category, e.g. glass, metal, fabric, plastic or wood, from a single image of a surface. Unlike other visual recognition tasks in computervision, it is difficult to find good, reliable features that can tell material categories apart. Our strategy is to use a rich set of low and mid-level features that capture various aspects of material appearance. We propose an augmented Latent Dirichlet Allocation (aLDA) model to combine these features under a Bayesian generative framework and learn an optimal combination of features. Experimental results show that our system performs material recognition reasonably well on a challenging material database, outperforming state-of-the-art material/texture recognition systems.
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