Using the edge detection techniques we propose a new enhancement scheme for noisy digital images. this uses inhomogeneous anisotropic diffusion scheme via the edge indicator provided by well known edge detection metho...
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ISBN:
(纸本)9781424442195
Using the edge detection techniques we propose a new enhancement scheme for noisy digital images. this uses inhomogeneous anisotropic diffusion scheme via the edge indicator provided by well known edge detection methods. Addition of a fidelity term facilitates the proposed scheme to remove the noise while preserving edges. this method is general in the sense that it can be incorporated into any of the nonlinear anisotropic diffusion methods. Numerical results show the promise of this hybrid technique on real and noisy images.
Symmetry and affine repetitions are common in scenes with man-made structures. In this paper we propose a technique to exploit affine repetitions in a 3D scene for reconstruction and view synthesis from a single image...
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ISBN:
(纸本)9781424442195
Symmetry and affine repetitions are common in scenes with man-made structures. In this paper we propose a technique to exploit affine repetitions in a 3D scene for reconstruction and view synthesis from a single image. Assuming three vanishing points in the image, we show how the 3D structure of multiple objects and their affine repetitions may be computed and used for synthesizing new views. the reconstructed objects may also be inserted in other scenes to create augmented images.
In this paper we address the problem of unsupervised learning of usual patterns of activities in an area under surveillance and detecting deviant patterns. We use video epitomes for segmenting foreground objects from ...
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ISBN:
(纸本)9781424442195
In this paper we address the problem of unsupervised learning of usual patterns of activities in an area under surveillance and detecting deviant patterns. We use video epitomes for segmenting foreground objects from background and obtain an approximate shape, trajectory and temporal information in the form of space-time patches. We apply pLSA for finding correlations among these patches to learn usual activities in the scene. We also extend pLSA to classify a novel video as usual or unusual.
In this paper a new robust watermarking scheme is proposed in multiresolution fractional Fourier transform domain using singular value decomposition. the watermark is embedded in the high frequency sub-band of the hos...
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ISBN:
(纸本)9781424442195
In this paper a new robust watermarking scheme is proposed in multiresolution fractional Fourier transform domain using singular value decomposition. the watermark is embedded in the high frequency sub-band of the host image at coarsest level. Although the schemes based on SVD are robust but fail under ambiguity attacks. In this attack, boththe owner and attacker can extract their watermark from the watermarked image. To prevent ambiguity, the normalized mass matrix is formed and embedded in the host image. In extraction, normalized mass matrix is extracted first and compared with original one. If the similarity is found then the singular values are extracted to construct the watermark. Experimental evaluation demonstrates that the proposed scheme is able to withstand a variety of attacks.
We describe a low memory, parallelizable implementation of graph cut based MRF energy minimization that solves pixel labeling problems. We first solve the problem on a low resolution version of the image and make use ...
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ISBN:
(纸本)9781424442195
We describe a low memory, parallelizable implementation of graph cut based MRF energy minimization that solves pixel labeling problems. We first solve the problem on a low resolution version of the image and make use of the technique of hierarchical graph cuts [2],[9] to obtain a narrow band of uncertainty in the high resolution image within which the labeling needs to be solved. We then sub-divide the narrow band into overlapping regions and solve the labeling problem for each of the regions separately. the overlapping regions are used to provide boundary conditions that force the labeling to be continuous. the key advantages of the method are low memory usage, cache friendliness and the Potential, for parallel execution. the solutions obtained are close to the global optimum. We demonstrate our method on the bi-label image segmentation and the multi-label image stitching problems.
this paper addresses the problem of retargeting, namely adapting large source images for effective viewing at a smaller size with possible applications to PDAs, or dynamic page layouts. Instead of extracting regions o...
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ISBN:
(纸本)9781424442195
this paper addresses the problem of retargeting, namely adapting large source images for effective viewing at a smaller size with possible applications to PDAs, or dynamic page layouts. Instead of extracting regions of interest for retargeting, the uninteresting parts are removed from the scene [8]. this is done by computing the RGB variance within non-overlapping 3x3 blocks and removing the block path with minimal variance cost using dynamic programming. It is shown that transformation to CIELAB space is more effective for visual interpretation of image content. the implementations are shown to be much faster than the seam carving approach of [8]. Schemes are also presented for speeding up the seam carving scheme itself.
the goal of this article is twofold. First, it deals with color image segmentation in hue-saturation space. A model for circular data is provided by the vM-Gauss distribution, which is a joint distribution of von-Mise...
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ISBN:
(纸本)9781424442195
the goal of this article is twofold. First, it deals with color image segmentation in hue-saturation space. A model for circular data is provided by the vM-Gauss distribution, which is a joint distribution of von-Mises and Gaussian distributions. the mixture of W-Gauss distributions is used to model hue-saturation data. After segmentation, a post processing based on both spectral and spatial similarity of clusters is applied to separate such identifiable objects in the image. the results and comparisons are shown on Berkeley segmentation dataset. the problem of text extraction from a color image is taken as an application of the proposed method. We use a laboratory made text image dataset to test the method.
Retinal images are widely used to manually or automatically detect and diagnose many diseases. Due to the complex imaging setup, there is a large luminosity and contrast variability within and across images. Here, we ...
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ISBN:
(纸本)9781424442195
Retinal images are widely used to manually or automatically detect and diagnose many diseases. Due to the complex imaging setup, there is a large luminosity and contrast variability within and across images. Here, we use the knowledge of the imaging geometry and propose an enhancement method for colour retinal images, with a focus on contrast improvement with no introduction of artifacts. the method uses non-uniform sampling to estimate the degradation and derive a correction factor from a single plane. We also propose a scheme for applying the derived correction factor to enhance all the colour planes of a given image. the proposed enhancement method has been tested on a publicly available dataset [8]. Results show marked improvement over existing methods.
In this paper a video coding scheme based on parametric compression of texture is proposed Each macro block is characterized either as an edge block, or as a non edge block containing texture. the non edge blocks are ...
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ISBN:
(纸本)9781424442195
In this paper a video coding scheme based on parametric compression of texture is proposed Each macro block is characterized either as an edge block, or as a non edge block containing texture. the non edge blocks are coded by modeling them as an auto-regressive process (AR). By applying the AR model in spatio-temporal domain, we ensure both spatial as well as temporal consistency. Edge blocks are encoded using the standard H.264/AVC. the proposed algorithm achieves upto 54.52% more compression as compared to the standard H.264/AVC at similar visual quality.
Usage of statistical classifiers, namely AdaBoost and its modifications, in object detection and pattern recognition is a contemporary and popular trend the computatiponal performance of these classifiers largely depe...
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ISBN:
(纸本)9781424442195
Usage of statistical classifiers, namely AdaBoost and its modifications, in object detection and pattern recognition is a contemporary and popular trend the computatiponal performance of these classifiers largely depends on low level image features they are using: both from the point of view of the amount of information the feature provides and the executional time of its evaluation. Local Rank Difference is an image feature that is alternative to commonly used Haar features. It is suitable for implementation in programmable (FPGA) or specialized (ASIC) hardware as well as graphics hardware (GPU). Additionally, as shown in this paper, it performs very well on common CPU's. the paper discusses the LRD features and their properties, describes an experimental implementation of LRD using the multimedia instruction set of current general-purpose processors, presents its empirical performance measures compared to alternative approaches, and suggests several notes on practical usage of LRD and proposes directions for future work.
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