this paper presents a new color document image binarization that is suitable for palm leaf manuscripts and color document images. the proposed method consists of two steps: a pre-processing procedure using low-pass Wi...
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ISBN:
(纸本)9781424442195
this paper presents a new color document image binarization that is suitable for palm leaf manuscripts and color document images. the proposed method consists of two steps: a pre-processing procedure using low-pass Wiener filter, and contrast adaptive binarization for segmentation of text from the background. Firstly, in the pre-processing stage, low-pass Wiener filter is used to eliminate noisy areas, smoothing of background texture as well as contrast enhancement between background and text areas. Finally, binarization is performed by using contrast adaptive binarization method in order to extract useful text information from low quality document images. the techniques are tested on a set of palm leaf manuscript images/color document images. the performance of the algorithm is demonstrated on by palm leaf manuscripts/color documents distorted with show-through effects, uneven background color and localized spot.
this paper presents a fundamentally new algebraic approach to the analysis and synthesis of discrete orthogonal basis functions. It provides the theoretical background to unify Fourier Gabor and discrete orthogonal po...
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ISBN:
(纸本)9781424442195
this paper presents a fundamentally new algebraic approach to the analysis and synthesis of discrete orthogonal basis functions. It provides the theoretical background to unify Fourier Gabor and discrete orthogonal polynomial moments. For the first time, a set of objective tests are proposed to measure the quality of basis functions. It consists of two main sections: the theoretical background on the generation and orthogonalization of basis functions together with a new solution for the computation of spectra from incomplete data, as well as the implementation of interpolation for all orthogonal basis functions;a new approach to discrete orthogonal polynomials, proving that there is one and only one unitary discrete polynomial basis. Furthermore, the concept of anisotropic moments is introduced and applied to 2D seismic data, which is an imageprocessing problem. the new polynomial basis is numerically better conditioned than the discrete cosine transform. this opens the door to new image compression algorithms, offering a higher compression ratio than the well known JPEG method, for the same numerical effort.
We propose a novel physically based method to simulate explosions and other compressible fluid phenomena. the method uses compressible Navier Stokes equations for modeling the explosion with a Semi-Lagrangian integrat...
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ISBN:
(纸本)9781424442195
We propose a novel physically based method to simulate explosions and other compressible fluid phenomena. the method uses compressible Navier Stokes equations for modeling the explosion with a Semi-Lagrangian integration method. the proposed integration method addresses the issues of stability and larger timesteps. this is achieved by modifying the Semi-Lagrangian method to reduce dissipation and increase accuracy, using improved interpolation and an error correction method. the proposed method allows the rendering of related phenomena like a fireball, dust and smoke clouds, and the simulation of solid interaction like rigid fracture and rigid body simulation. Our method is flexible enough to afford substantial artistic control over the behavior of the explosion.
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.
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 withthe 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.
image based methods are a new approach for solving problems in mobile robotics. Instead of building a metric (3D) model of the environment, these methods work directly in the sensor (image) space. the environment is r...
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ISBN:
(纸本)9781424442195
image based methods are a new approach for solving problems in mobile robotics. Instead of building a metric (3D) model of the environment, these methods work directly in the sensor (image) space. the environment is represented as a topological graph in which each node contains an image taken at some pose in the workspace, and edges connect poses between which a simple path exists. this type of representation is highly scalable and is also well suited to handle the data association problems that effect metric model based methods. In this paper we present an efficient, adaptive method for qualitative localization using content based image retrieval techniques. In addition, we demonstrate an algorithm which can convert this topological graph into a metric model of the environment by incorporating information about loop closures.
the paper presents a hybrid thresholding approach for binarization and enhancement of degraded documents. Historical documents contain information of great cultural and scientific value. But such documents are frequen...
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ISBN:
(纸本)9781424442195
the paper presents a hybrid thresholding approach for binarization and enhancement of degraded documents. Historical documents contain information of great cultural and scientific value. But such documents are frequently degraded over time. Digitized degraded documents require specialized processing to remove different kinds of noise and to improve readability. the approach for enhancing degraded documents uses a combination of two thresholding algorithms. First, iterative global thresholding is applied to the smoothed degraded image until the stopping criteria is reached then a threshold selection method from gray level histogram is used to binarize the image. the next step is detecting areas where noise still remains and applying iterative thresholding locally. A method to improve the quality of textual information in the document is also done as a post processing stage, thus making the approach efficient and better suited for character recognition applications.
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.
One of the foremost requisite for human perception and computervision task is to get an image with all objects in focus. the image fusion process, as one of the solutions, allows getting a clear fused image from seve...
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ISBN:
(纸本)9781479915880
One of the foremost requisite for human perception and computervision task is to get an image with all objects in focus. the image fusion process, as one of the solutions, allows getting a clear fused image from several images acquired with different focus levels of a scene. In this paper, a novel framework for multi-focus image fusion is proposed, which is computationally simple since it realizes only in the spatial domain. the proposed framework is based on the fractal dimensions of the images into the fusion process. the extensive experiments on different multifocus image sets demonstrate that it is consistently superior to the conventional image fusion methods in terms of visual and quantitative evaluations.
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