Current denoising techniques use the classical orthonormal wavelets for decomposition of an image corrupted with additive white Gaussian noise, upon which various thresholding strategies are built. The use of availabl...
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ISBN:
(纸本)9781424442195
Current denoising techniques use the classical orthonormal wavelets for decomposition of an image corrupted with additive white Gaussian noise, upon which various thresholding strategies are built. The use of available biorthogonal wavelets in image denoising is less common because of their poor performance. hi this paper, we present a method to design image-matched biorthogonal wavelet bases and report on their potential for denoising. We have conducted experiments on various image datasets namely Natural, Satellite and Medical with the designed wavelets using two existing thresholding strategies. Test results front comparing the performance of matched and fixed biorthogonal wavelets show an average improvement of 35% in MSE for low SNR values (0 to 18db) in every dataset. This improvement was also seen in the PSNR and visual comparisons. This points to the importance of matching when using wavelet-based denoising.
This paper presents a new approach to achieve the performance improvement for the traditional palmprint authentication approaches. The cohort information is used in the matching stage but only when the matching scores...
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ISBN:
(纸本)9781424442195
This paper presents a new approach to achieve the performance improvement for the traditional palmprint authentication approaches. The cohort information is used in the matching stage but only when the matching scores are inadequate to generate reliable decisions. The cohort information can also be utilized to achieve the significant performance improvement for the combination of modalities and this is demonstrated from the experimental results in this paper. The rigorous palmprint authentication results presented in this paper are the best in the literature and confirm the utility of significant information that can be extracted from the imposter scores. The statistical estimation of confidence level for the palmprint matching requires an excellent match between the theoretical distribution and the real score distribution. The performance analysis presented in this paper, from over 29.96 million imposter matching scores, suggests that Beta-Binomial function can more accurately model the distribution of real palmprint matching scores.
We propose a new method to compress the geometry component of 3D animation sequence. It is based on the Linear Discriminant Analysis (LDA) of the animation geometry data. The redundancy across the animation frames has...
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ISBN:
(纸本)9781424442195
We propose a new method to compress the geometry component of 3D animation sequence. It is based on the Linear Discriminant Analysis (LDA) of the animation geometry data. The redundancy across the animation frames has been exploited by using the LDA in the temporal direction. Owing to the redundancy between the frames of a class, the covariance matrix of that class for the LDA computation may become singular. To overcome this drawback, we first transform the data into a new basis using the Principal Component Analysis (PCA) and then apply the LDA on a few principal components. The reconstruction is simple and involves two stages: firstly for the LDA and then for the PCA. The experimental results show that the proposed method has the advantage of better reconstruction error at high compression ratios.
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
State of art document segmentation algorithms employ adhoc solutions which use some document properties and iteratively segment the document image. These solutions need to be adapted frequently and sometimes fail to p...
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ISBN:
(纸本)9781424442195
State of art document segmentation algorithms employ adhoc solutions which use some document properties and iteratively segment the document image. These solutions need to be adapted frequently and sometimes fail to perform well for complex scripts. This calls for a generalized solution that achieves a one shot segmentation that is globally optimal. This paper describes one such solution based on the optimization problem of spectral partitioning which makes the decision of proper segmentation based on the Spectral properties of the pairwise similarity matrix. The solution described in the paper is shown to be general, global and closed form. The claims have been demonstrated on 142 page images from a Telugu book, in a script set in both poetry and prose layouts. This particular class of scripts has been proved to be challenging for the existing state of the art algorithms, where the proposed solution achieves significant results.
In this paper, we propose a novel framework for automated analysis of surveillance videos. By analysis, we imply summarizing and mining of the information in the video for learning usual patterns and discovering unusu...
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ISBN:
(纸本)9781424442195
In this paper, we propose a novel framework for automated analysis of surveillance videos. By analysis, we imply summarizing and mining of the information in the video for learning usual patterns and discovering unusual ones. We approach this video analysis problem by acknowledging that a video contains information at multiple levels and in multiple attributes. Each such component and co-occurrences of these component values play an important role in characterizing an event as usual or unusual. Therefore, we cluster the video data at multiple levels of abstraction and in multiple attributes and view these clusters as a summary of the information in the video. We apply cluster algebra to mine this summary from multiple perspectives and to adapt association learning for automated selection of components because of which the event is unusual. We also propose a novel incremental clustering algorithm.
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.
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.
We present a novel learning-based framework for detecting interesting events in soccer videos. The input to the system is a raw soccer video. We have learning at three levels - learning to detect interesting low-level...
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ISBN:
(纸本)9781424442195
We present a novel learning-based framework for detecting interesting events in soccer videos. The input to the system is a raw soccer video. We have learning at three levels - learning to detect interesting low-level features from image and video data using Support Vector Machines (hereafter SVMs), and a hierarchical Conditional Random Field(hereafter CRF-) based methodology to learn the dependencies of mid-level features and their relation with the low level features, and high level decisions ('interesting events') and their relation with the mid-level features: all on the basis of training video data. Descriptors are spatio-temporal in nature - they can be associated with a region in an image or a set of frames. Temporal patterns of descriptors characterise an event. We apply this framework to parse soccer videos into Interesting (a goal or a goal miss) and Non-Interesting videos. We present results of numerous experiments in support of the proposed strategy.
We propose a novel framework for object detection and localization in images containing appreciable clutter and occlusions. The problem is cast in a statistical hypothesis testing framework. The image under test is co...
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ISBN:
(纸本)9781424442195
We propose a novel framework for object detection and localization in images containing appreciable clutter and occlusions. The problem is cast in a statistical hypothesis testing framework. The image under test is converted into a set of local features using affine invariant local region detectors, described using the popular SIFT descriptor Due to clutter and occlusions, this set is expected to contain features which do not belong to the object. We sample subsets of local features from this set and test for the alternate hypothesis of object present against the null hypothesis of object absent. Further, we use a method similar to the recently proposed spatial scan statistic to refine the object localization estimates obtained from the sampling process. We demonstrate the results of our method on the two datasets TUD Motorbikes and TUD Cars. TUD Cars database has background clutter TUD Motorbikes dataset is recognized to have substantial variation in terms of scale, back-ground, illumination, viewpoint and occlusions.
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