In this paper the fusion of multimodal images into one greylevel image is aimed at. A multiresolution technique, based on the wavelet multiscale edge representation is applied. The fusion consists of retaining only th...
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ISBN:
(纸本)0819437646
In this paper the fusion of multimodal images into one greylevel image is aimed at. A multiresolution technique, based on the wavelet multiscale edge representation is applied. The fusion consists of retaining only the modulus maxima of the wavelet coefficients from the different bands and combining them. After reconstruction, a synthetic image is obtained that contains the edge information from all bands simultaneously. Noise reduction is applied by removing the noise-related modulus maxima. In several experiments on test images and multispectral satellite images, we demonstrate that the proposed technique outperforms mapping techniques, as PCA and SOM and other wavelet-based fusion techniques.
Although many wavelet-based pitch detection methods have been proposed in the literatures, there still remains a need to investigate new wavelet-based methods for more accurate and more robust pitch determination. In ...
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ISBN:
(纸本)0780370414
Although many wavelet-based pitch detection methods have been proposed in the literatures, there still remains a need to investigate new wavelet-based methods for more accurate and more robust pitch determination. In this paper, an improved wavelet-based method is developed for extraction of pitch information in noisy speech. At each decomposition in the wavelet transform, an aliasing compensation algorithm is applied to approximate and detail signals, in which the distortion of aliasing due to downsampling and upsampling operations of the wavelet transform is eliminated. In addition, this paper utilizes the concept of spatial correlation function used in signal denoising to improve the performance of pitch detection in noisy environment. It is shown in various experimental results that this new type of method has a considerable performance improvement compared with other conventional methods and wavelet-based methods.
This paper studies the adaptive wavelet design for fabric defect detection. In order to achieve translation invariance and more flexible design, the wavelet design focused on nonsubsampled wavelet transform. We design...
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ISBN:
(纸本)0780370414
This paper studies the adaptive wavelet design for fabric defect detection. In order to achieve translation invariance and more flexible design, the wavelet design focused on nonsubsampled wavelet transform. We design the wavelet filters under the constraints that the analysis filters are power complementary, and the wavelet has only one vanishing moment, which corresponds to a multiscale edge deteter. Based on lattice structure factorization, the design of power complementary filter turn out to be unconstrainted optimization of lattice coefficients. Adaptive wavelets are designed for five kinds of fabric defects in the experiments. Comparing the proposed method with adaptive wavelet design for defect detection based on orthogonal wavelet transform, our design largely improve the ratio of wavelet transform energy between the defect area and the background, and achieve a robust and accurate detection of fabric defects.
Data decorrelation and energy compaction are the two fundamental characteristics of wavelets that led to wavelet based image compression models. wavelet transform is not a perfect whitening transform;but it is viewed ...
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ISBN:
(纸本)0819450804
Data decorrelation and energy compaction are the two fundamental characteristics of wavelets that led to wavelet based image compression models. wavelet transform is not a perfect whitening transform;but it is viewed as an approximation to Karhunen-Loeve transform (KLT). In general, decorrelation does not imply statistical independence. Thus, a wavelet transform results in coefficients which exhibit inter and intra band dependencies. The energy compaction property of a wavelet is reflected in the coding performance, which can be measured by its coding gain. This paper investigates the above two important aspects of bi-orthogonal wavelets in the context of lossy compression. This investigation suggests that simple predictive models are sufficient to capture the dependencies exhibited by the wavelet coefficients. This paper also compares, the metrics that measure the performance of bi-orthogonal wavelets in lossy coding schemes.
This paper explores the application of wavelets to a variety of real life problems and more specifically to imageprocessing problems. A general review of the construction and analysis of wavelet analysis will be pres...
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ISBN:
(纸本)081942840X
This paper explores the application of wavelets to a variety of real life problems and more specifically to imageprocessing problems. A general review of the construction and analysis of wavelet analysis will be presented. The issues like multiresolution analysis in the context of sensor integration and pattern recognition and other salient features of the images using wavelets will be discussed in detail.
We define and characterize a frame-like stable decomposition for subspaces in a separable Hilbert space. We call in pseudoframes for subspaces (PFFS). Properties of PFFS are discussed. A necessary and sufficient condi...
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ISBN:
(纸本)0819429139
We define and characterize a frame-like stable decomposition for subspaces in a separable Hilbert space. We call in pseudoframes for subspaces (PFFS). Properties of PFFS are discussed. A necessary and sufficient condition for the construction is provided. An analytical formula for the construction of PFFS is also derived. An example is studied both as a motivation of the theoretical study of such pseudoframes and as an actual construction. Potential applications of PFFS are discussed.
wavelet transform is an efficient way to perform multiresolution signal decomposition with a good time and frequency resolution and simple generation of families of functions to generate bases of the space of square s...
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ISBN:
(纸本)085296692X
wavelet transform is an efficient way to perform multiresolution signal decomposition with a good time and frequency resolution and simple generation of families of functions to generate bases of the space of square summable sequences. The basis functions used in the wavelet transform are all affine transformed versions of the original function. The compactly supported wavelets are defined from a scaling function that is the solution of a fractal-like equation. The multiresolution fractal coders lead to better results, both in terms of rate-distortion curves and visual quality.
Sparse signal representations and compressed sensing have found use in a large number of applications including image compression. Compressed sensing exploits the sparsity of naturally occurring images to reduce the v...
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ISBN:
(纸本)9781467321976;9781467321969
Sparse signal representations and compressed sensing have found use in a large number of applications including image compression. Compressed sensing exploits the sparsity of naturally occurring images to reduce the volume of the data by using less number of measurements. Inspired by this, we propose a new framework for image compression that combines compressed sensing theory with wavelet and vector quantization. wavelet transform is used to sparsify the input image while measurement vectors generated from the sparse vectors are transmitted using vector quantization. Simulation experiments are carried out to analyze the effects of various parameters on the image reconstruction quality. Results obtained have been found to be quite promising.
This paper describes an approach for accomplishing sub- octave wavelet analysis and its discrete implementation for noise reduction and feature enhancement. Sub-octave wavelet transforms allow us to more closely chara...
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In this paper, the problem of segmentation of smooth images has been studied using multiresolution analysis. The approximated image intensity function is modeled as a quadratic polynomial with additive noise within lo...
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ISBN:
(纸本)0819416274;9780819416278
In this paper, the problem of segmentation of smooth images has been studied using multiresolution analysis. The approximated image intensity function is modeled as a quadratic polynomial with additive noise within local windows. The analysis has been carried out with the aid of a new orthonormal wavelet basis introduced in this paper. A procedure has been developed to approximate an image at a coarse resolution by dropping the components of the image in such a way that small bumps at finer resolutions are suppressed. An image segmentation scheme is proposed. It performs initial segmentation on a coarse approximation of the image, and then updates the segments of the image at a finer resolution. The proposed algorithm has been tested on a variety of real images such as human faces, natural scenes, and medical images.
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