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.
Mural images are noisy and consist of faint and broken lines. Here we propose a novel technique for straight and curve line detection and also an enhancement algorithm for deteriorated mural images. First we compute s...
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ISBN:
(纸本)9781479915880
Mural images are noisy and consist of faint and broken lines. Here we propose a novel technique for straight and curve line detection and also an enhancement algorithm for deteriorated mural images. First we compute some statistics on gray image using oriented templates. the outcome of the process are taken as a strength of the line at each pixel. As a result some unwanted lines are also detected in the texture region. Based on Gestalt law of continuity we propose an anisotropic refinement to strengthen the true lines and to suppress the unwanted ones. A modified bilateral filter is employed to remove the noises. Experimental result shows that the approach is robust to restore the lines in the mural images.
In this paper, an approach for the classification of different hardwood species of open access database, using texture feature extraction and supervised machine learning technique has been implemented. Edges of comple...
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ISBN:
(纸本)9781479915880
In this paper, an approach for the classification of different hardwood species of open access database, using texture feature extraction and supervised machine learning technique has been implemented. Edges of complex cellular structure of microscopic images of hardwood are enhanced withthe application of Gabor filter, and Gray Level Co-occurrence Matrix (GLCM) as an effective texture feature extraction technique is being revalidated. About, 44 features have been extracted from GLCM;these features have been further normalized in the range [0.1, 1]. Multilayer Perceptron Backpropagation Artificial Neural Network have been used for classification. Experiments conducted on 25 wood species have resulted in recognition accuracy of about 88.60% and 92.60% using Levenberg-Marquardt backpropagation training function with two different datasets for training, validation and testing ratio (70%, 15%, 15% and 80%, 10%, 10%) respectively. Proposed methodology can be extended with optimized machine learning techniques for online identification of wood.
Large space with many cameras require huge storage and computational power to process these data for surveillance applications. In this paper we propose a distributed camera and processing based face detection and rec...
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ISBN:
(纸本)9781479915880
Large space with many cameras require huge storage and computational power to process these data for surveillance applications. In this paper we propose a distributed camera and processing based face detection and recognition system which can generate information for finding spatiotemporal movement pattern of individuals over a large monitored space. the system is built upon Hadoop Distributed File System using map reduce programming model. A novel key generation scheme using distance based hashing technique has been used for distribution of the face matching task. Experimental results have established effectiveness of the technique.
this paper is aimed at exploring the potential of using discriminatory primitives containing words for the task of detecting skilled forgeries. We consider handwritten Devanagri documents for this work. We have obtain...
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ISBN:
(纸本)9781479915880
this paper is aimed at exploring the potential of using discriminatory primitives containing words for the task of detecting skilled forgeries. We consider handwritten Devanagri documents for this work. We have obtained experimental handwriting data from subjects who have contributed handwriting samples in their natural handwriting. Other authors are asked to imitate the writing style of the subjects to produce a skilled forgery sample. Most of the literature dealing with writer recognition focus on signatures and very few reports have addressed the problem of detecting forgeries for handwritten indian scripts. We also use multiple words based classification for the targeted task of forgery detection. Our experiments show encouraging results.
In this paper, we address the problem of separating the diffuse and specular reflection components of complex textured surfaces from a single color image. Unlike most previous approaches that assume accurate knowledge...
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ISBN:
(纸本)9781479915880
In this paper, we address the problem of separating the diffuse and specular reflection components of complex textured surfaces from a single color image. Unlike most previous approaches that assume accurate knowledge of illumination source color for this task, we analyze errors in source color information to perform robust separation. the analysis leads to a simple, efficient and robust algorithm to estimate the diffuse and specular components using the estimated source color. the algorithm is completely automatic and does not need explicit color segmentation or color boundary detection as required by many existing methods. Results on complex textured images show the effectiveness of the proposed algorithm for robust reflection component separation.
this paper presents a novel method for discovery and recognition of hairstyles in a collection of colored face images. We propose the use of Agglomerative clustering for automatic discovery of distinct hairstyles. Our...
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ISBN:
(纸本)9781479915880
this paper presents a novel method for discovery and recognition of hairstyles in a collection of colored face images. We propose the use of Agglomerative clustering for automatic discovery of distinct hairstyles. Our method proposes automated approach for generation of hair, background and face-skin probability-masks for different hairstyle category without requiring manual annotation. the probability-masks based density estimates are subsequently applied for recognizing the hairstyle in a new face image. the proposed methodology has been verified with a synthetic dataset of approximately thousand images, randomly collected from the Internet.
the video coding standard H.264 uses Contextbased Adaptive Variable Length Coding (CAVLC) as one of its entropy encoding techniques. this paper proposes VLSI architecture for CAVLC algorithm. the designed hardware mee...
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ISBN:
(纸本)9781479915880
the video coding standard H.264 uses Contextbased Adaptive Variable Length Coding (CAVLC) as one of its entropy encoding techniques. this paper proposes VLSI architecture for CAVLC algorithm. the designed hardware meets the required speed of H.264 without compromising the hardware cost. the CAVLC encoder works at a maximum clock frequency of 126 MHz when implemented in Xilinx 10.1i, Virtex-5 technology. the speed is quite appreciable when compared to other existing works. the implemented architecture meets the required rate for processing of HD-1080 format video sequence.
In this paper we have proposed methods for restoration of artifacts called Partial Color Artifact(PCA) and Blotches which appear frequently in old video films. the PCA occurs due to partial loss of information in the ...
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ISBN:
(纸本)9781479915880
In this paper we have proposed methods for restoration of artifacts called Partial Color Artifact(PCA) and Blotches which appear frequently in old video films. the PCA occurs due to partial loss of information in the upper color layers of the video film. As the inner most color layer is unaffected, the information present in this inner most color layer of the film aids in the reconstruction of damaged pixels from previously reconstructed frames. In Blotch artifact the pixel information is completely lost. the proposed Blotch reconstruction method is based on sparse recovery of signals from small number of measurements. Our blotch reconstruction process is computationally efficient because the image is segmented into non overlapping blocks and reconstruction is done block wise.
this paper proposes a novel framework that unifies the concept of sparsity of a signal over a properly chosen basis set and the theory of signal reconstruction via compressed sensing in order to obtain a high-resoluti...
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ISBN:
(纸本)9781479915880
this paper proposes a novel framework that unifies the concept of sparsity of a signal over a properly chosen basis set and the theory of signal reconstruction via compressed sensing in order to obtain a high-resolution image derived by using a single down-sampled version of the same image. First, we enforce sparse overcomplete representations on the low-resolution patches of the input image. then, using the sparse coefficients as obtained above, we reconstruct a high-resolution output image. A blurring matrix is introduced in order to enhance the incoherency between the sparsifying dictionary and the sensing matrices which also resulted in better preservation of image edges and other textures. When compared withthe similar techniques, the proposed method yields much better result both visually and quantitatively.
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