Indexing echocardiogram videos at different levels of structure is essential for providing efficient access to their content for browsing and retrieval purposes. We present a novel approach for the automatic identific...
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Indexing echocardiogram videos at different levels of structure is essential for providing efficient access to their content for browsing and retrieval purposes. We present a novel approach for the automatic identification of the views of the heart from the content of the echocardiogram videos. In this approach the structure of the heart is represented by the constellation of its parts (chambers) under the different views. The statistical variations of the parts in the constellation and their spatial relationships are modeled using Markov Random Field models. A discriminative method is then used for view recognition which fuses the assessments of a test image by all the view-models. To the best of our knowledge, this is the first work addressing the analysis of the echocardiogram videos for the purpose of indexing their content. The method presented could be used for multiple-object recognition when the objects are represented by their parts and there are structural similarities between them.
We propose an approach to include contextual features for labeling images, in which each pixel is assigned to one of a finite set of labels. The features are incorporated into a probabilistic framework which combines ...
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We propose an approach to include contextual features for labeling images, in which each pixel is assigned to one of a finite set of labels. The features are incorporated into a probabilistic framework which combines the outputs of several components. Components differ in the information they encode. Some focus on the image-label mapping, while others focus solely on patterns within the label field. Components also differ in their scale, as some focus on fine-resolution patterns while others on coarser, more global structure. A supervised version of the contrastive divergence algorithm is applied to learn these features from labeled image data. We demonstrate performance on two real-world image databases and compare it to a classifier and a Markov random field.
The automated segmentation of images into semantically meaningful parts requires shape information since low-level feature analysis alone often fails to reach this goal. We introduce a novel method of shape constraine...
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The automated segmentation of images into semantically meaningful parts requires shape information since low-level feature analysis alone often fails to reach this goal. We introduce a novel method of shape constrained image segmentation which is based on mixtures of feature distributions for color and texture as well as probabilistic shape knowledge. The combined approach is formulated in the framework of Bayesian statistics to account for the robustness requirement in image understanding. Experimental evidence shows that semantically meaningful segments are inferred, even when image data alone gives rise to ambiguous segmentations.
The goal of this work is to take an image such as the one in Figure 1(a), detect a human figure, and localize his joints and limbs (b) along with their associated pixel masks (c). In this work we attempt to tackle thi...
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The goal of this work is to take an image such as the one in Figure 1(a), detect a human figure, and localize his joints and limbs (b) along with their associated pixel masks (c). In this work we attempt to tackle this problem in a general setting. The dataset we use is a collection of sports news photographs of baseball players, varying dramatically in pose and clothing. The approach that we take is to use segmentation to guide our recognition algorithm to salient bits of the image. We use this segmentation approach to build limb and torso detectors, the outputs of which are assembled into human figures. We present quantitative results on torso localization, in addition to shortlisted full body configurations.
In this paper, we present a unified framework for modeling intrinsic properties of face images for recognition. It is based on the quotient image (QI) concept, in particular on the existing works of QI [1, 2], Spheric...
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In this paper, we present a unified framework for modeling intrinsic properties of face images for recognition. It is based on the quotient image (QI) concept, in particular on the existing works of QI [1, 2], Spherical Harmonic[13, 14, 15], [16, 17], Image Ratio [3, 5, 6, 7]and Retinex [4, 9]. Under this framework, we generalize these previous works into two new algorithms: (1) Non-Point Light Quotient Image (NPL-QI) extends QI to deal with non-point light sources by modeling non-point light directions using spherical harmonic bases;(2) Self-Quotient Image (S-QI) extends QI to perform illumination subtraction without the need for alignment and no shadow assumption. Experimental results show that our algorithms can significantly improve the performance of face recognition under varying illumination conditions.
In this work we consider face recognition from face motion manifolds. An information-theoretic approach with Resistor-Average Distance (RAD) as a dissimilarity measure between distributions of face images is proposed....
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computers should be able to detect and track the articulated 3-D pose of a human being moving through a video sequence. Current tracking methods often prove slow and unreliable, and many must be initialized by a human...
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Tracking articulated objects in image sequences remains a challenging problem, particularly in terms of the ability to localize the individual parts of an object given self-occlusions and changes in viewpoint. In this...
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Tracking articulated objects in image sequences remains a challenging problem, particularly in terms of the ability to localize the individual parts of an object given self-occlusions and changes in viewpoint. In this paper we propose a two-dimensional spatio-temporal modeling approach that handles both self-occlusions and changes in viewpoint. We use a Bayesian framework to combine pictorial structure spatial models with hidden Markov temporal models. Inference for these combined models can be performed using dynamic programming and sampling methods. We demonstrate the approach for the problem of tracking a walking person, using silhouette data taken from a single camera viewpoint. Walking provides both strong spatial (kinematic) and temporal (dynamic) constraints, enabling the method to track limb positions in spite of simultaneous self-occlusion and viewpoint change.
We present a real-time and high-precision face recognition system using an image processing LSI chip:Visconti[Visconti: Multi-VLIW Image recognition Processor]. The system is compact and operates at low power, making ...
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Several computervision problems, such as some of photometric problems and the problem of affine structure from motion, are formulated as fitting linear subspace(s) to point data in a multi-dimensional space. In ideal...
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