We propose a wavelet-based texture classification system. Texture descriptors are local energy measures within the feature images obtained by projecting the samples on Dyadic Frames of Directional wavelets. Rotation i...
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ISBN:
(纸本)0819437646
We propose a wavelet-based texture classification system. Texture descriptors are local energy measures within the feature images obtained by projecting the samples on Dyadic Frames of Directional wavelets. Rotation invariant features are obtained by taking the Fourier expansion of the subsets of components of the original feature vectors concerning each considered scale (or resolution) separately. Three different classification schemes have been compared: the Euclidean, the weighted Euclidean and the KNN classifiers. Performances have been evaluated on a set of 13 Brodatz textures, from which both a training set and a test set have been extracted. Results are presented in the form of confusion matrices. The KNN classifier provides the globally best performance, with an average recognition rate around the 96% for the original non-rotated test set, and 88% when the rotated versions are considered. Its simplicity and accuracy renders the proposed method highly suited for multimedia applications, as content-based image retrieval.
We investigate several issues surrounding the general question of when a function in a finitely generated shift invariant subspace of L-2(R) can be determined by certain of its sample values just as a function bandlim...
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ISBN:
(纸本)0819437646
We investigate several issues surrounding the general question of when a function in a finitely generated shift invariant subspace of L-2(R) can be determined by certain of its sample values just as a function bandlimited to [-1/2, 1/2] can be expressed in terms of its integer samples. The main theme here is how answers to this question depend on general properties of the generators of the shift invariant space, such as orthogonality properties, scaling relations, smoothness and so forth. One of the main issues that Re address is the question of how to control aliasing error.
Empirical wavelets transform (EWT) is a fully adaptive signal-analysis approach, which is similar to the empirical mode decomposition (EMD) but has a consolidated mathematical theory, and is appealing in designing aut...
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Empirical wavelets transform (EWT) is a fully adaptive signal-analysis approach, which is similar to the empirical mode decomposition (EMD) but has a consolidated mathematical theory, and is appealing in designing automatic algorithm for a variety of signalprocessing tasks. EWT first estimates the frequency components presented in the given signal, then, computes the boundaries and extracts the oscillatory components based on the computed boundaries. Because of the excellent performance of the EWT in decomposing the nonlinear and non-stationary signals, it has been successfully applied into a number of problems. The last six years have seen the development of EWT. This paper presents a general overview of the recent advancements made in research on the EWT algorithm and its state-of-the-art applications in a wide range of areas, such as machine fault diagnosis, seismic data analysis, imageprocessing, power system monitoring, and medical disease diagnosis, which aims at providing some comprehensive references for reader concerning with EWT. We place emphasis on the applications of using such signal-analysis algorithm throughout with illustrative examples. Finally, the potential avenues for the future trends and directions associated with EWT are discussed.
Multiresolution methods are deeply related to imageprocessing, biological and computer vision, and scientific computing. The curvelet transform is a multiscale directional transform that allows an almost optimal nona...
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Multiresolution methods are deeply related to imageprocessing, biological and computer vision, and scientific computing. The curvelet transform is a multiscale directional transform that allows an almost optimal nonadaptive sparse representation of objects with edges. It has generated increasing interest in the community of applied mathematics and signalprocessing over the years. In this article, we present a review on the curvelet transform, including its history beginning from wavelets, its logical relationship to other multiresolution multidirectional methods like contourlets and shearlets, its basic theory and discrete algorithm. Further, we consider recent applications in image/video processing, seismic exploration, fluid mechanics, simulation of partial different equations, and compressed sensing.
This paper presents a new method for light field applications such as content replacement and fusion in the gradient domain. This approach is inspired by successful gradient domain based image and video editing techni...
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This paper presents a new method for light field applications such as content replacement and fusion in the gradient domain. This approach is inspired by successful gradient domain based image and video editing techniques. A necessary and important part of gradient-based solutions is recovering the signal of interest from artificially generated, and typically non-integrable, gradient data. As such, a new algorithm is developed to reconstruct a light field from a given gradient data set. In the algorithm, first, the 4D Haar wavelet decomposition of the light field is obtained from the given gradient data. Then, the light field is obtained from a wavelet synthesis step. This algorithm is intended as a building block for gradient-based light field editing methods, and as such, its performance is analysed on a set of benchmark light field data sets. The proposed reconstruction algorithm is an essential part in developing solutions for two light field problems: light field editing and light field fusion. Results show that processing light fields in the gradient domain offers significant advantages over processing in the intensity domain.
When performing registrations, it is often crucial to maintain certain structure of the template data T-the data being deformed into the subject data S-as well as to keep the deformation field smooth. Current approach...
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ISBN:
(纸本)0819437646
When performing registrations, it is often crucial to maintain certain structure of the template data T-the data being deformed into the subject data S-as well as to keep the deformation field smooth. Current approaches to registration often impose smoothness through heuristic means, but building it into the model has proven to be more difficult due mainly to computational constraints. In this paper, we view the registration problem as the following: find a deformation nu so that cost C(T, S, nu) is minimized. We model the deformation field as nu := Sigma (n)(i=1) c(i)phi (i), in which the {c(i)} are multipliers for basis functions {phi (i)} whose shape and size are chosen adaptively. This gives flexibility in incorporating automatic basis function selection methods and a priori information about T and S into the model. By selecting and placing basis functions appropriately, the number of basis functions required to produce a good deformation is reduced over uniform grid methods. Thus, this method should provide both improved computational speed and improved accuracy over such methods.
We propose an optical image watermarking scheme based on orbital angular momentum(OAM)*** topological charges(TCs,l)of OAM,as multiple cryptographic sub-keys,are embedded into the host image along with the watermark *...
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We propose an optical image watermarking scheme based on orbital angular momentum(OAM)*** topological charges(TCs,l)of OAM,as multiple cryptographic sub-keys,are embedded into the host image along with the watermark ***,the Arnold transformation is employed to further enhance the security and the scrambling time(m)is also served as another cryptographic *** watermark image is embedded into the host image by using the discrete wavelet transformation(DWT)and singular value decomposition(SVD)***,the interference image is utilized to further enhance *** imperceptibility of our proposed method is analyzed by using the peak signal-to-noise ratio(PSNR)and the histogram of the watermarked host *** demonstrate robustness,a series of attack tests,including Gaussian noise,Poisson noise,salt-and-pepper noise,JPEG compression,Gaussian lowpass filtering,cropping,and rotation,are *** experimental results show that our proposed method has advanced security,imperceptibility,and robustness,making it a promising option for optical image watermarking applications.
We are interested in learning efficient codes to represent classes of different images. The image classes are modeled using an ICA (independent component analysis) mixture model that assumes that the data was generate...
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ISBN:
(纸本)0819437646
We are interested in learning efficient codes to represent classes of different images. The image classes are modeled using an ICA (independent component analysis) mixture model that assumes that the data was generated by several mutually exclusive data classes whose components are a mixture of non-Gaussian sources. The parameters of the model can be adapted using an approximate expectation maximization approach to maximize the data likelihood. We demonstrate that this method can learn classes of efficient codes to represent images that contain a variety of different structures. The learned codes can be used for image compression, de-noising and classification tasks. Compared to standard image coding methods, the ICA mixture model gives better encoding results because the codes are adapted to the structure of the data.
This paper aims at reviewing the recent published works dealing with industrial applications of wavelet and, more generally speaking, multiresolution analysis. After a quick recall in a simple overview of the basics o...
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ISBN:
(纸本)0819455601
This paper aims at reviewing the recent published works dealing with industrial applications of wavelet and, more generally speaking, multiresolution analysis. After a quick recall in a simple overview of the basics of wavelet transform and of its main variations, some of its applications are reviewed domain by domain, beginning with signalprocessing, continuous and discrete wavelet transform proceeding with imageprocessing and applications. More than 150 recent papers are presented in these two sections.
We have successfully compressed audio signals using wavelet packets based on a recently developed fast wavelet transform (FWT) scheme using circular convolution with an adaptive hybrid filter/basis system. This algori...
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ISBN:
(纸本)0819416274;9780819416278
We have successfully compressed audio signals using wavelet packets based on a recently developed fast wavelet transform (FWT) scheme using circular convolution with an adaptive hybrid filter/basis system. This algorithm gives perfect reconstruction of the data; edge effects are removed entirely. As a result, the quality of audio signal compression is much improved. To illustrate this, we present results from our comparison study where we compressed a test signal using these `circular wavelet packets' and wavelet packets based on the standard FWT.
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