In this contribution, we propose an optoelectronic hit/miss morphological transform for real-time quality control by image moire analysis, that integrates a VanderLugt optical correlator and a digital signal processor...
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ISBN:
(纸本)0819425214
In this contribution, we propose an optoelectronic hit/miss morphological transform for real-time quality control by image moire analysis, that integrates a VanderLugt optical correlator and a digital signal processor associated to a vector co-processor. The procedure for real-time defect detection is a three stage process. The first step is to enhance the moire image, using the wavelet decomposition and a multiresolution approach. The second step is to automatically segment the enhanced moire image, using the moment preserving thresholding algorithm. The third step is to apply the morphological hit/mass transform to directly recognize moire images that correspond to defectuous objects. This new procedure of defect detection by global analysis of moire image data has been compared to another new technique that is based on multidimensional supervised classification of optical correlations between the test object moire image and reference moire images.
In this paper, the novel Q-wave algorithm for image coding is proposed. Q-wave, which provides progressive transmission, is designed with the aim of limiting the computational complexity at the expenses of a slight qu...
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ISBN:
(纸本)0819437646
In this paper, the novel Q-wave algorithm for image coding is proposed. Q-wave, which provides progressive transmission, is designed with the aim of limiting the computational complexity at the expenses of a slight quality degradation with respect to popular coders such as SPIHT. It is particularly suitable for low bit rate compression, and is then promising for applications such as intraframe video coding, Internet browsing, image transmission over band-limited channels. Moreover, Q-wave can be applied also to non standard wavelet decompositions without modifications.
In this paper, we establish a mathematical connection between dyadic-wavelet-based contrast enhancement and traditional unsharp masking. Our derivation is completely based in the discrete domain. These findings may pr...
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ISBN:
(纸本)0819416274;9780819416278
In this paper, we establish a mathematical connection between dyadic-wavelet-based contrast enhancement and traditional unsharp masking. Our derivation is completely based in the discrete domain. These findings may provide a better theoretical understanding of these algorithms, and facilitate the acceptance of multiscale enhancement techniques applied to medical imaging.
The problem of image feature extraction for classification is difficult because of the high dimensionality inherent in image data. By extracting only relevant image features we reduce the dimensionality of the problem...
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ISBN:
(纸本)0819425915
The problem of image feature extraction for classification is difficult because of the high dimensionality inherent in image data. By extracting only relevant image features we reduce the dimensionality of the problem and improve classification accuracy. We further enhance classification performance by finding an optimal representation of the extracted image features which maximizes separability distance among classes. The principal tools used are Fourier series, wavelet packets, local discriminant basis analysis, and neural networks.
wavelet transform is a powerful and useful mathematical tool for signalprocessing. In this paper detailed description of procedures for numerical integration and derivation in Haar domain has been done. These procedu...
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ISBN:
(纸本)0819450804
wavelet transform is a powerful and useful mathematical tool for signalprocessing. In this paper detailed description of procedures for numerical integration and derivation in Haar domain has been done. These procedures are necessary both in control systems analysis, and, especially, for control design and development. A detailed comparison between classical methods of evaluation and the Haar way is presented and critically discussed.
Overcomplete wavelet representations have become increasingly popular for their ability to provide highly sparse and robust descriptions of natural signals. We describe a method for incorporating an overcomplete wavel...
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ISBN:
(纸本)0819450804
Overcomplete wavelet representations have become increasingly popular for their ability to provide highly sparse and robust descriptions of natural signals. We describe a method for incorporating an overcomplete wavelet representation as part of a statistical model of images which includes a sparse prior distribution over the wavelet coefficients. The wavelet basis functions are parameterized by a small set of 2-D functions. These functions are adapted to maximize the average log-likelihood of the model for a large database of natural images. When adapted to natural images, these functions become selective to different spatial orientations, and they achieve a superior degree of sparsity on natural images as compared with traditional wavelet bases. The learned basis is similar to the Steerable Pyramid basis, and yields slightly higher SNR for the same number of active coefficients. Inference with the learned model is demonstrated for applications such as denoising, with results that compare favorably with other methods.
Besov spaces classify signals and images through the Besov norm, which is based on a deterministic smoothness measurement. Recently, we revealed the relationship between the Besov norm and the likelihood of an indepen...
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ISBN:
(纸本)0819437646
Besov spaces classify signals and images through the Besov norm, which is based on a deterministic smoothness measurement. Recently, we revealed the relationship between the Besov norm and the likelihood of an independent generalized Gaussian wavelet probabilistic model. In this paper, we extend this result by providing an information-theoretic interpretation of the Besov norm as the Shannon codelength for signal compression under this probabilistic mode. This perspective unites several seemingly disparate signal/imageprocessing methods, including denoising by Besov norm regularization, complexity regularized denoising, minimum description length (MDL) processing, and maximum smoothness interpolation. By extending the wavelet probabilistic model (to a locally adapted Gaussian model), we broaden the notion of smoothness space to more closely characterize real-world data. The locally Gaussian model leads directly to a powerful wavelet-domain Wiener filtering algorithm for denoising.
The use of wavelet thresholding has been investigated with much success in the areas of denoising, density estimation, image restoration, etc. Significant attention has been given to wavelet thresholding as a signal d...
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ISBN:
(纸本)0780362985
The use of wavelet thresholding has been investigated with much success in the areas of denoising, density estimation, image restoration, etc. Significant attention has been given to wavelet thresholding as a signal denoising technique. The algorithm is simple and provides good results. The 2-D discrete wavelet transform (DWT) and its relatives have been used to generalize the denoising methods to images. However the DWT is limited in its representation of directional information like edges and some types of texture. In this paper Ne propose the use of a Directional Filter Bank [ii for image denoising under the same premise as wavelet thresholding: small magnitude subband coefficients represent noise and can be replaced with zeros while large coefficients reflect, in our case, strong signal content in a given direction. We show that the Directional Filter Bank is capable of preserving edge information better than DWT based techniques while effectively removing noise. The proposed technique provides sharp images with higher perceptual quality.
The two-dimensional discrete wavelet transform has a huge number of applications in image-processing techniques. Until now, several papers compared the performance of such transform on graphics processing units (GPUs)...
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ISBN:
(纸本)9781509021758
The two-dimensional discrete wavelet transform has a huge number of applications in image-processing techniques. Until now, several papers compared the performance of such transform on graphics processing units (GPUs). However, all of them only dealt with lifting and convolution computation schemes. In this paper, we show that corresponding horizontal and vertical lifting parts of the lifting scheme can be merged into non-separable lifting units, which halves the number of steps. We also discuss an optimization strategy leading to a reduction in the number of arithmetic operations. The schemes were assessed using the OpenCL and pixel shaders. The proposed non-separable lifting scheme outperforms the existing schemes in many cases, irrespective of its higher complexity.
Fast algorithms performing time-scale analysis of multivariate functions are discussed. The algorithms employ univariate wavelets and involve a directional parameter, namely the angle of rotation. Both the rotation st...
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ISBN:
(纸本)0819429139
Fast algorithms performing time-scale analysis of multivariate functions are discussed. The algorithms employ univariate wavelets and involve a directional parameter, namely the angle of rotation. Both the rotation steps and the wavelet analysis/synthesis steps in the algorithms require a number of computations proportional to the number of data involved. The rotation and wavelet techniques are used for the segregation of wanted and unwanted components in a seismic signal. As an illustration, the rotation and wavelet methods are applied to a synthetic shot record.
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