This paper analyses two-dimensional image rotation algorithms on the basis of execution time and mean square error produced. Effect of forward and reverse mapping on algorithm complexity is discussed. Single and multi...
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imageprocessing is widely used in many applications, including medical imaging, industrial manufacturing and security systems. In these applications, the size of the image is often very large, the processing time sho...
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ISBN:
(纸本)354029032X
imageprocessing is widely used in many applications, including medical imaging, industrial manufacturing and security systems. In these applications, the size of the image is often very large, the processing time should be very small and the real-time constraints should be met. Therefore, during the last decades, there has been an increasing demand to exploit parallelism in applications. It is possible to explore parallelism along three axes: data-level parallelism (DLP), instruction-level parallelism (ILP) and task-level parallelism (TLP). This paper explores the limitations and bottlenecks of increasing support for parallelism along the DLP and ILP axes in isolation and in combination. To scrutinize the effect of DLP and ILP in our architecture (template), an area model based on the number of ALUs (ILP) and the number of processing elements (DLP) in the template is defined, as well as a performance model. Based on these models and the template, a set of kernels of imageprocessingapplications has been studied to find Pareto optimal architectures in terms of area and number of cycles via multi-objective optimization.
This paper describes an efficient and adaptive method of threshold estimation for removing impulse noise from images, based on Double Density Wavelet Transform (DDWT). The performance of image de-noising algorithms us...
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This paper describes an efficient and adaptive method of threshold estimation for removing impulse noise from images, based on Double Density Wavelet Transform (DDWT). The performance of image de-noising algorithms using wavelet transforms can be improved significantly by fixing an optimum threshold value, based on the analysis of the statistical parameters of subband coefficients. In this proposed method, the choice of the threshold estimation is carried out by analyzing the statistical parameters of the wavelet subband coefficients like standard deviation, arithmetic mean and geometrical mean. Here the noisy image is first decomposed into many levels to obtain different frequency bands using DDWT. Then soft thresholding method is used to remove the noisy coefficients, by fixing the optimum threshold value by the proposed method. Experimental results on several test images by using the proposed method show that, the proposed method yields significantly superior image quality and better Peak signal-to-Noise Ratio (PSNR). Some comparisons with the best available results will be given in order to illustrate the effectiveness of the proposed algorithm.
This paper presents a system for capturing and rendering a dynamic image-based representation called the plenoptic video. It is a simplified light field for dynamic environments, where user viewpoints are constrained ...
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This paper presents a system for capturing and rendering a dynamic image-based representation called the plenoptic video. It is a simplified light field for dynamic environments, where user viewpoints are constrained to the camera plane of a linear array of video cameras. Important issues such as multiple camera calibration, real-time compression, decompression and rendering are addressed. The system consists of a camera array of eight Sony CCx-Zll CCD cameras and eight Pentium 4 1.8-GHz computers connected together through a 100 Base-T local area network. It is possible to perform software-assisted real-time MPEG-2 compression at a resolution of (720 x 480). Using selective transmission, we are able to stream continuously plenoptic video with (256 x 256) resolution at a rate of 15 Its over the network. For rendering from raw data on the hard disk, real-time rendering can be achieved with a resolution of (720 x 480) and a rate of 15 f/s. A new compression algorithm using both temporal and spatial predictions is also proposed for the efficient compression of the plenoptic videos. Experimental results demonstrate the usefulness of the proposed parallel processing based system in capturing and rendering high-quality dynamic image-based representations using off-the-shelf equipment, and its potential applications in visualization and immersive television systems.
Hash functions are frequently called message digest functions. Their purpose is to extract a short binary string from a large digital message. A key feature of conventional cryptographic (and other) hashing algorithms...
Hash functions are frequently called message digest functions. Their purpose is to extract a short binary string from a large digital message. A key feature of conventional cryptographic (and other) hashing algorithms such as message digest 5 (MD5) and secure hash algorithm 1 (SHA-1) is that they are extremely sensitive to the message; i.e., changing even one bit of the input message will change the output dramatically. However, multimedia data such as digital images undergo various manipulations such as compression and enhancement. An image hash function should instead take into account the changes in the visual domain and produce hash values based on the image's visual appearance. Such a function would facilitate comparisons and searches in large image databases. Other applications of a perceptual hash lie in content authentication and watermarking. This dissertation proposes a unifying framework for multimedia signal hashing. The problem of media hashing is divided into two stages. The first stage extracts media-dependent intermediate features that are robust under incidental modifications while being different for perceptually distinct media with high probability. The second stage performs a media-independent clustering of these features to produce a final hash. This dissertation focuses on feature extraction from natural images such that the extracted features are largely invariant under perceptually insignificant modifications to the image (i.e. robust). An iterative geometry preserving feature detection algorithm is developed based on an explicit modeling of the human visual system via end-stopped wavelets. For the second stage. I show that the decision version of the feature clustering problem is NP-complete. Then, for any perceptually significant feature extractor, I develop polynomial time clustering algorithms based on a greedy heuristic. Existing algorithms for image/media hashing exclusively employ either cryptographic or signalprocessing methods. A pu
For optical iris recognition, the eigen-images correlation recognition method is modified. The 2-D separable approximations of wavelet packet bases are constructed with the help of the cascade algorithm. Expanding the...
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For optical iris recognition, the eigen-images correlation recognition method is modified. The 2-D separable approximations of wavelet packet bases are constructed with the help of the cascade algorithm. Expanding the scale of basis selection, mutli-mother multi-vanishing moment joint best bases are chosen from the basis set of 25 mother wavelets including the mothers constructed by the lifting scheme. Using the corresponding eigen-images generated and the post-processing method based on statistic features, optical experiment is implemented. The experimental result agrees with the simulation result.
We discuss a Continuous Curvelet Transform (CCT), a transform f -> Gamma(f) (a, b, theta) of functions f(x(1), x(2)) on R-2 into a transform domain with continuous scale a > 0, location b is an element of R-2, a...
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We discuss a Continuous Curvelet Transform (CCT), a transform f -> Gamma(f) (a, b, theta) of functions f(x(1), x(2)) on R-2 into a transform domain with continuous scale a > 0, location b is an element of R-2, and orientation theta is an element of [0, 2 pi). Here Gamma(f) (a, b, theta) = < f, gamma(ab theta)> projects f onto analyzing elements called curvelets gamma(ab theta) which are smooth and of rapid decay away from an a by root a rectangle with minor axis pointing in direction theta. We call them curvelets because this anisotropic behavior allows them to 'track' the behavior of singularities along curves. They are continuum scale/space/orientation analogs of the discrete frame of curvelets discussed in [E.J. Candes, F. Guo, New multiscale transforms, minimum total variation synthesis: applications to edge-preserving image reconstruction, signal Process. 82 (2002) 1519-1543;E.J. Candes, L. Demanet, Curvelets and Fourier integral operators, C. R. Acad. Sci. Paris, Ser. I (2003) 395-398;E.J. Candes, D.L. Donoho, Curvelets: a surprisingly effective nonadaptive representation of objects with edges, in: A. Cohen, C. Rabut, L.L. Schumaker (Eds.), Curve and Surface Fitting: Saint-Malo 1999, Vanderbilt Univ. Press, Nashville, TN, 2000]. We use the CCT to analyze several objects having singularities at points, along lines, and along smooth curves. These examples show that for fixed (x(0), theta(0)), Gamma(f) (a, x(0), theta(0)) decays rapidly as a -> 0 if f is smooth near x(0), or if the singularity of f at x(0) is oriented in a different direction than theta(0). Generalizing these examples, we show that decay properties of Gamma(f) (a, x(0), theta(0)) for fixed (x(0), theta(0)), as a -> 0 can precisely identify the wavefront set and the H-m-wavefront set of a distribution. In effect, the wavefront set of a distribution is the closure of the set of (x(0), theta(0)) near which Gamma(f) (a, x, theta) is not of rapid decay as a -> 0;the H-m-wavefront set is the closure of
Interflation of the function f(x1,...,xn)of the n variables with help of the its traces (and traces of its derivatives of order &le N) on the M surfaces the dimension m is recovery (possible, exactly) f. If m = 0 ...
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ISBN:
(纸本)0889864772
Interflation of the function f(x1,...,xn)of the n variables with help of the its traces (and traces of its derivatives of order &le N) on the M surfaces the dimension m is recovery (possible, exactly) f. If m = 0 this is interpolation on M points (for n ≥ 1). If m = 1 (for n ≥ 2) it is interlineation (blending function interpolation) on M lines . In this paper the review of last achievements and some applications interflation, interlineation functions and blending functions approximation for construction the economical algorithms in multidimensional signalprocessing.
A fully reconfigurable architecture for realizing thermal imaging system is proposed. This architecture provides a generic solution to any signalprocessing issues related to thermal imaging and also provides the adva...
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A fully reconfigurable architecture for realizing thermal imaging system is proposed. This architecture provides a generic solution to any signalprocessing issues related to thermal imaging and also provides the advantage of low power design and take care of any sensor up-gradation. The proposed architecture is implemented by using xilinx Virtex II 2000K gate FPGA and 320 x 256 elements InSb IRFPA. The system can store up to six gain and offset tables which can be optimized for different environmental conditions and as well us allow the up-gradation of offset coefficients dynamically. image frames are presented which shows the successful implementation of the architecture.
In this paper, two approaches for image denoising that take advantages of neighboring dependency in the wavelet domain are studied. The first approach is to take into account the higher order statistical coupling betw...
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In this paper, two approaches for image denoising that take advantages of neighboring dependency in the wavelet domain are studied. The first approach is to take into account the higher order statistical coupling between neighboring wavelet coefficients and their corresponding coefficients in the parent level. The second is based on multivariate statistical modeling. The estimation of the clean coefficients is obtained by a general rule using Bayesian approach. Various estimation expressions can be obtained by a priori probability distribution, called multivariate generalized Gaussian distribution (MGGD). The experimental results show that both of our methods give comparatively higher peak signal to noise ratio (PSNR) as well as little visual artifact for monochrome images. In addition, we extend our approaches to a denoising algorithm for color image that has multiple color components. The proposed color denoising algorithm is a framework to consider the correlations between color components yet using the existing monochrome denoising method without modification. Denoising results in this framework give noticeable better improvement than in the case when the correlation between color components is not considered.
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