Tensor fields specifically, matrix valued data sets, have recently attracted increased attention in the fields of imageprocessing.computer vision, visualization and medical imaging. In this paper, we present a novel...
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Tensor fields specifically, matrix valued data sets, have recently attracted increased attention in the fields of imageprocessing.computer vision, visualization and medical imaging. In this paper, we present a novel definition of tensor "distance" grounded in concepts from information theory and incorporate it in the segmentation of tensor-valued images. In some applications, a symmetric positive definite (SPD) tensor at each point of a tensor valued image can be interpreted as the covariance matrix of a local Gaussian distribution. Thus, a natural measure of dissimilarity between SPD tensors would be the KL divergence or its relative. We propose the square root of the J-divergence (symmetrized KL) between two Gaussian distributions corresponding to the tensors being compared that leads to a novel closed form expression. Unlike the traditional Frobenius norm-based tensor distance, our "distance" is affine invariant, a desirable property in many applications. We then incorporate this new tensor "distance" in a region based active contour model for bimodal tensor field segmentation and show its application to the segmentation of diffusion tensor magnetic resonance images (DT-MRI) as well as for the texture segmentation problem in computer vision. Synthetic and real data experiments are shown to depict the performance of the proposed model.
We present a simple and universal camera calibration method. Instead of extensive setups we are exploiting the accurate angular positions of fixed stars. High precision is achieved by compensating the interfering erro...
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We present a simple and universal camera calibration method. Instead of extensive setups we are exploiting the accurate angular positions of fixed stars. High precision is achieved by compensating the interfering error sources. Our approach uses a star catalog and requires a single input image only. No additional user input information such as focal length, exposure date or position is required. Fully automatic processing.and fast convergence is achieved by performing three consecutive steps. First, a star segmentation and centroid finding algorithm extracts the sub-pixel positions of the luminaries. Second, an initial solution for the most essential parameters is determined by combinatorial analysis. Finally, the Levenberg-Marquardt algorithm is applied to solve the resulting non-linear system. Experimental results with several digital consumer cameras demonstrate high robustness and accuracy. The introduced method is advisable for applications where large calibration targets are required.
We propose a face reconstruction technique that produces models that not only look good when texture mapped, but are also metrically accurate. Our method is designed to work with short uncalibrated video or movie sequ...
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We propose a face reconstruction technique that produces models that not only look good when texture mapped, but are also metrically accurate. Our method is designed to work with short uncalibrated video or movie sequences, even when the lighting is poor resulting in specularities and shadows that complicate the algorithm's task. Our approach relies on optimizing the shape parameters of a sophisticated PCA based model given pairwise image correspondences as input. All that is required is enough relative motion between camera and subject so that we can derive structure from motion. By matching the results against laser scanning data, we will show that its precision is excellent and can be predicted as a junction of the number and quality of the correspondences. This is important if one wishes to obtain the appropriate compromise between processing.speed and quality of the results. Furthermore, our method is in fact not specific to faces and could equally be applied to any shape for which a shape model controlled with relatively small number of parameters exists.
Plan-view projection of real-time depth imagery can improve the statistics of its intrinsic 3D data, and allows for cleaner separation of occluding and closely-interacting people. We build a probabilistic, real-time m...
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Plan-view projection of real-time depth imagery can improve the statistics of its intrinsic 3D data, and allows for cleaner separation of occluding and closely-interacting people. We build a probabilistic, real-time multi-person tracking system upon a plan-view image substrate that well preserves both shape and size information of foreground objects. The tracking's robustness derives in part from its "plan-view template" person models, which capture detailed properties of people's body configurations. We demonstrate that these same person models, obtained with a single compact stereo camera unit, may also be used for fast recognition of body pose and activity. Principal components analysis is used to extract plan-view "eigenposes", onto which person models, extracted during tracking, are projected to produce a compact representation of human body configuration. We then formulate pose recognition as a classification problem, and use support vector machines (SVMs) to quickly distinguish between, for example, different directions people are facing, and different body poses such as standing, sitting, bending over, crouching, and reaching. The SVM outputs are transformed to probabilities and integrated across time in a probabilistic framework for real-time activity recognition.
This work explores a statistical basis for a process often described in computer vision: image segmentation by region merging following a particular order in the choice of regions. We exhibit a particular blend of alg...
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This work explores a statistical basis for a process often described in computer vision: image segmentation by region merging following a particular order in the choice of regions. We exhibit a particular blend of algorithmics and statistics whose error is, as we formally show, close to the best possible. This approach can be approximated in a very fast segmentation algorithm for processing.images described using most common numerical feature spaces. Simple modifications of the algorithm allow to cope with occlusions and/or hard noise levels. Experiments on grey-level and color images, obtained with a short C-code, display the quality of the segmentations obtained.
We address the problem of vector-valued image regularization with variational methods and PDE's. From the study of existing formalisms, we propose a unifying framework based on a very local interpretation of the r...
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ISBN:
(纸本)0769519008
We address the problem of vector-valued image regularization with variational methods and PDE's. From the study of existing formalisms, we propose a unifying framework based on a very local interpretation of the regularization processes. The resulting equations are then specialized into new regularization PDE's and corresponding numerical schemes that respect the local geometry of vector-valued images. They are finally applied on a wide variety of imageprocessing.problems, including color image restoration, inpainting, magnification and flow visualization.
This paper presents a novel background subtraction method for detecting foreground objects in dynamic scenes involving swaying trees and fluttering flags. Most methods proposed so far adjust the permissible range of t...
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This paper presents a novel background subtraction method for detecting foreground objects in dynamic scenes involving swaying trees and fluttering flags. Most methods proposed so far adjust the permissible range of the background image variations according to the training samples of background images. Thus, the detection sensitivity decreases at those pixels having wide permissible ranges. If we can narrow the ranges by analyzing input images, the detection sensitivity can be improved. For this narrowing, we employ the property that image variations at neighboring image blocks have strong correlation, also known as "cooccurrence". This approach is essentially different from chronological background image updating or morphological postprocessing. Experimental results for real images demonstrate the effectiveness of our method.
This paper presents an implicit similarity-based approach to registration of significantly dissimilar images, acquired by sensors of different modalities. The proposed algorithm introduces a robust matching criterion ...
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This paper presents an implicit similarity-based approach to registration of significantly dissimilar images, acquired by sensors of different modalities. The proposed algorithm introduces a robust matching criterion by aligning the locations of gradient maxima. The alignment is formulated as a parametric variational optimization problem which is solved iteratively by considering the intensities of a single image. The locations of the maxima of the second image's gradient are used as initialization., We were able to robustly estimate affine and projective global motions using 'coarse to fine' processing. even when the images are characterized by complex space varying intensity transformations. Finally, we present the registration of real images, which were taken by multi-sensor and multi-modality using affine and projective motion models.
Background subtraction is the first step of many video surveillance applications. What is considered background varies by application, and may include regular, systematic, or complex motions. This paper explores the u...
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Background subtraction is the first step of many video surveillance applications. What is considered background varies by application, and may include regular, systematic, or complex motions. This paper explores the use of several different local spatio-temporal models of a background, defined at each pixel in the image. We present experiments with real image data and conclude that appropriate local representations are sufficient to make background models of complicated real world motions. Empirical studies illustrate, for example, that an optical flow-based model is able to detect emergency vehicles whose motion is different from those typically observed in traffic scenes. We conclude that "different models are appropriate for different scenes", but give criteria by which one can choose which model will be best.
One of the main tasks in content-based image retrieval (CBIR) is to reduce the gap between low-level visual features and high-level human concepts. This paper presents a new semi-supervised EM algorithm (NSS-EM), wher...
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One of the main tasks in content-based image retrieval (CBIR) is to reduce the gap between low-level visual features and high-level human concepts. This paper presents a new semi-supervised EM algorithm (NSS-EM), where the image distribution in feature space is modeled as a mixture of Gaussian densities. Due to the statistical mechanism of accumulating and processing.meta knowledge, the NSS-EM algorithm with long-term learning of mixture model parameters can deal,with the cases where users may mislabel images during relevance feedback. Our approach that integrates mixture model of the data, relevance feedback and long-term learning helps to improve retrieval performance. The concept learning is incrementally refined with increased retrieval experiences. Experiment results on Corel database show the efficacy of our proposed concept learning approach.
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