A novel nonlinear cooperative approach to image denoising and restoration is presented. Samples from the image field with similar characteristics are first grouped into clusters by first performing image decomposition...
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ISBN:
(纸本)9781424442195
A novel nonlinear cooperative approach to image denoising and restoration is presented. Samples from the image field with similar characteristics are first grouped into clusters by first performing image decomposition based on the Mumford-Shah model using a total variational framework and performing fuzz), c-means clustering within each image partition. Samples within each cluster are then aggregated using an cooperative Bayesian estimation method based on information from all the samples to provide a nonlinear estimate of the original image. the proposed method exploits information redundancy within each cluster to denoise and restore the original image. Furthermore, the proposed cooperative Bayesian estimation method is capable of suppressing noise and reducing image degradation while preserving image detail by utilizing intra-cluster statistics. the experimental results using different types of images demonstrate that the proposed algorithm provides state-of-the-art image denoising performance in terms of both peak signal-to-noise ratio (PSNR) and subjective visual quality
Visual attention is an indispensable component of complex vision tasks. When looking at a complex scene, our ocular perception is confronted with a large amount of data that needs to be broken down for processing by o...
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ISBN:
(纸本)9781479915880
Visual attention is an indispensable component of complex vision tasks. When looking at a complex scene, our ocular perception is confronted with a large amount of data that needs to be broken down for processing by our psychovisual system. Selective visual attention provides a mechanism for serializing the visual data, allowing for sequential processing of the content of the scene. A Bottom-Up computational model is described that simulates the psycho-visual model of saliency based on features of intensity and color. the method gives sequential priorities to objects which other computational models cannot account for. the results demonstrate a fast execution time, full resolution maps and high detection accuracy. the model is applicable on both natural and artificial images.
Range images captured from range scanning devices such as laser scanners or PMD (photonic mixer device) cameras, often possess drawbacks of having low resolution and/or missing regions due to occlusions, reflectivity ...
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ISBN:
(纸本)9781424442195
Range images captured from range scanning devices such as laser scanners or PMD (photonic mixer device) cameras, often possess drawbacks of having low resolution and/or missing regions due to occlusions, reflectivity limited scanning area, sensor imperfections etc. In this work, we address boththe issues in a single framework. We employ Bayesian regularization for resolution enhancement and inpainting in a general multi-image super-resolution scenario. We modify the traditional image formation model used in image/range super-resolution to account for the missing regions. this modification is important to couple the inpainting process with super-resolution. We also stress the importance of prior information in the integration and note that we require the priors to constrain the solution differently for inpainting and for super-resolution. the proposed inhomogeneous prior handles the requirements for inpainting as well as super-resolution. the modification of the imaging model and the formulation of the inhomogeneous prior are both important for the success of the integration. Our results show inpainting of large missing regions, reduction in distortions and good preservation of details at the high-resolution.
In shape recognition, a multiscale description provides more information about the object, increases discrimination power and immunity to noise. In this paper, we develop a new multiscale Fourier-based object descript...
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ISBN:
(纸本)9781424442195
In shape recognition, a multiscale description provides more information about the object, increases discrimination power and immunity to noise. In this paper, we develop a new multiscale Fourier-based object description in 2-D space using a low-pass Gaussian filter (LPGF) and a high-pass Gaussian filter (HPGF), separately. Using the LPGF, at different scales, represents the inner and central part of an object more than the boundary. On the other hand using the HPGF, at different scales, represents the boundary and exterior parts of an object more than the central part. Our algorithms are also organized to achieve size, translation and rotation invariance. Evaluation indicates that representing the boundary and exterior parts more than the central part using the HPGF performs better than the LPGF based multiscale representation, and in comparison to Zernike moments and elliptic Fourier descriptors with respect to increasing noise.
the GPUs pack high computation power and a restricted architecture into easily available hardware today. they are now used as computation co-processors and come with programming models that treat them as standard para...
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ISBN:
(纸本)9781424442195
the GPUs pack high computation power and a restricted architecture into easily available hardware today. they are now used as computation co-processors and come with programming models that treat them as standard parallel architectures. We explore the problem of real time ray casting of large deformable models (over a million triangles) on large displays (a million pixels) on an off-the-shelf GPU in this paper Ray casting is an inherently, parallel and highly compute intensive operation. We build a GPU-efficient three-dimensional data structure for this purpose and a corresponding algorithm that uses it for fast ray casting. We also present fast methods to build the data structure on the SIMD GPUs, including a fast multi-split operation. We achieve real-time ray-casting of a million triangle model onto a million pixels on current Nvidia GPUs using the CUDA model. Results are presented on the data structure building and ray casting on a number of models. the ideas presented here are likely to extend to later models and architectures of the GPU as well as to other multi core architectures.
Detection of representative frames, also called key-frames, is essential for efficient indexing, browsing and retrieval of video data and also for video summarization. Once a video stream is segmented into shots, the ...
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ISBN:
(纸本)9781424442195
Detection of representative frames, also called key-frames, is essential for efficient indexing, browsing and retrieval of video data and also for video summarization. Once a video stream is segmented into shots, the representative frames or key-frames for the shot are selected. the number of such frames in a shot may vary depending on the variation it? the content. thus, for a wide variety of shots automatic selection of suitable number of representative frames still remains a challenge. In this work, we propose a novel scheme for key-frame detection by dividing an available shot into subshots using hypothesis testing and majority voting. Each subshot is supposed to be uniform in terms of visual content. then for each subshot, the frame rendering the highest fidelity is extracted as the key-frame. Experimental result shows that the scheme works satisfactorily for a wide variety of shots.
In this work we propose a new formulation for hyperspectral denoising based on the Blind Compressed Sensing (BCS) framework. BCS learns the sparsifying basis during signal recovery combining the advantages of standard...
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ISBN:
(纸本)9781467385640
In this work we propose a new formulation for hyperspectral denoising based on the Blind Compressed Sensing (BCS) framework. BCS learns the sparsifying basis during signal recovery combining the advantages of standard sparse recovery with dictionary learning. We show that our proposed formulation yields better results than a state-of-the-art technique hyperspectral denoising both in terms of PSNR (more than 1dB improvement) and visual quality.
We describe a novel method for human activity segmentation and interpretation in surveillance applications based on Gabor filter-bank features. A complex human activity is modeled as a sequence of elementary human act...
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ISBN:
(纸本)9781424442195
We describe a novel method for human activity segmentation and interpretation in surveillance applications based on Gabor filter-bank features. A complex human activity is modeled as a sequence of elementary human actions like walking, running, jogging, boxing, hand-waving etc. Since human silhouette can be modeled by a set of rectangles, the elementary human actions can be modeled as a sequence of a set of rectangles with different orientations and scales. the activity segmentation is based on Gabor filter-bank features and normalized spectral clustering. the feature trajectories of an action category are learnt from training example videos using Dynamic Time Warping. the combined segmentation and the recognition processes are very efficient as boththe algorithms share the same framework and Gabor features computed for the former can be used for the later We have also proposed a simple shadow detection technique to extract good silhouette which is necessary for good accuracy of an action recognition technique.
Complete design and implementation of a robust palm biometrics recognition and verification system has been presented. the paper attempts to scientifically develop a comprehensive set of hand geometry features and dev...
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ISBN:
(纸本)9781424442195
Complete design and implementation of a robust palm biometrics recognition and verification system has been presented. the paper attempts to scientifically develop a comprehensive set of hand geometry features and develop an original algorithm from a fundamental level to robustly compute a selected set of features so as to minimize palm placement effect. these features are combined with chrominance features, to achieve recognition and verification accuracy, significant with respect to the current state in palm biometrics research. the algorithm has been kept robust, simple and computationally efficient while the implementation is relatively inexpensive. the experiments on recognition, Principal Components Analysis (PCA) and verification, on a dataset of 100 users strongly confirm the utility of robustly calculating a comprehensive set Of scientifically selected hand geometry features. the results uncover the potential of hand geometry features and confirm that the system can be used in medium to high security environments.
In this paper we propose to summarize videos based on key frames. We improve upon the histogram and pixel difference based approach with fuzzy rule based approach and also give a new approach which reduces the computa...
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ISBN:
(纸本)9781479915880
In this paper we propose to summarize videos based on key frames. We improve upon the histogram and pixel difference based approach with fuzzy rule based approach and also give a new approach which reduces the computation of framewise differences. We test our methods using fidelity ratio and compression ratio on videos of sports from YouTube and UCF sports dataset, videos of commercials and sitcoms. T he results of our methods are seen to be comparable to other state of the art approaches.
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