Analysis of surface profile generally involves filtering to separate short wavelengths from medium and long wavelengths. This is usually accomplished using digital filters. The wavelet filter is an ideal means to sepa...
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ISBN:
(纸本)0819422134
Analysis of surface profile generally involves filtering to separate short wavelengths from medium and long wavelengths. This is usually accomplished using digital filters. The wavelet filter is an ideal means to separate the profile into different bands. The space frequency localization and multiscale presentation of different wavelength components is useful in manufacturing process control and in establishing relationship between surface texture and function. The use of wavelet filter to analyze surface is explored in this paper. This paper deals with the evaluation of suitable wavelet basis for analyzing surface texture of machined surfaces using wavelet filter. The multiscale surface features are analyzed using wavelet filter to explore the potential use of wavelet filter in monitoring of manufacturing process and feature detection in engineering surfaces.
In order to satisfy the needs of new multimedia applications, the problem of content-based coding has to be addressed. In this paper a new approach of object interior coding is proposed. It is based on an arbitrarily-...
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ISBN:
(纸本)0780331923
In order to satisfy the needs of new multimedia applications, the problem of content-based coding has to be addressed. In this paper a new approach of object interior coding is proposed. It is based on an arbitrarily-shaped subband transform followed by a generalized embedded zerotree wavelet algorithm. It is shown that the proposed technique achieves good compression results and has additional properties such as being computationally efficient, keeping the same dimensionality in the transformed domain, being perfect reconstruction, and allowing a perfect rate control. In addition a lossless mode can be defined by using an appropriate filter bank.
In this work, we present a new family of image compression algorithms derived from Shapiro's embedded zerotree wavelet (EZW) coder. These new algorithms introduce robustness to transmission errors into the bit str...
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New techniques for developing more efficient noise reduction schemes are presented and implemented by applying the wavelet transform (WT) to a series of stationary and non- stationary signals. Their effectiveness is i...
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ISBN:
(纸本)0819422134
New techniques for developing more efficient noise reduction schemes are presented and implemented by applying the wavelet transform (WT) to a series of stationary and non- stationary signals. Their effectiveness is illustrated with specific applications to both real and synthetic seismic data, and the superiority over Fourier transform (FT) based methods is demonstrated. These methods aim at the efficient reduction of the effects that surface waves, airwaves, and direct waves can have on the interpretation of a seismic record. We first apply the WT on each trace in a common- depth-point gather and then perform stacking in the WT domain and compute both the mean and median transforms. Then, the signal-to-noise ratio of the stacked transforms is estimated and used as a criterion to improve the quality of the transformed data, and finally the total energy in the stacked WT plane is computed and redistributed in order to boost weak events. The advantage of stacking in the WT domain is that it allows for detection of weak reflections overpowered by high amplitude surface and air waves. Additionally, it is shown that by frequency modulating a mother wavelet, further attenuation of surface waves, airwaves, and first breaks may be achieved.
This paper is concerned with the problem of determining performance of a wavelet-based hybrid neurosystem trained to provide efficient feature extraction and signal classification. The hybrid network consists of a par...
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ISBN:
(纸本)0819422134
This paper is concerned with the problem of determining performance of a wavelet-based hybrid neurosystem trained to provide efficient feature extraction and signal classification. The hybrid network consists of a parallel array of neurosystems. Each neurosystem is constructed with three single neural networks; two of which are feature extraction networks, and the other is a classification network, are provided with magnitude and location information of the wavelet transform coefficients, respectively, and are trained with self-organizing rules. Their outputs are then presented to the classification network for pattern recognition. Based on the topological maps provided by the feature extraction neural networks, the back-propagation algorithm is used to train the third network for pattern recognition. The combination of wavelet, wavelet transform, and hybrid neural network architecture and advanced training algorithms in the design makes the system unique and provides high classification accuracy. In this paper, system performance is shown to be intrinsically related to basis kernel function used in feature extraction. A method for selecting the optimal basis function and a performance analysis using simulated data under various noise condition are presented and compared against other pattern recognition techniques.
We developed a method of weighted wavelet packets for separation of small, low contrast signals from large, inhomogeneous background. Our method was applied to the enhancement of microcalcifications on digital mammogr...
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ISBN:
(纸本)0819422134
We developed a method of weighted wavelet packets for separation of small, low contrast signals from large, inhomogeneous background. Our method was applied to the enhancement of microcalcifications on digital mammograms, which appear as small bright spots superimposed on the background representing the structure of breast tissue, for improvement of the performance of our computer-aided diagnosis scheme for detection of clustered microcalcifications. Our method first approximates signals if interest by a set of wavelets that are extracted from a wavelet packet dictionary by means of the matching pursuit algorithm. The selected set of wavelets is then subjected to a supervised learning process for optimization of the weights assigned to individual time-frequency tiles for enhancement of the microcalcifications and suppression of the background structures. In an analysis of 82 regions of interest extracted from our mammographic database, our new method showed a sensitivity of 92 percent and a specificity of 75 percent. Our new method is shown to perform better than our previous method based on the fixed-weight, orthogonal wavelet transform.
Mosaic techniques have been used to combine two or more signals into a new one with an invisible seam, and with as little distortion of each signal as possible. Multiresolution representation is an effective method fo...
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Mosaic techniques have been used to combine two or more signals into a new one with an invisible seam, and with as little distortion of each signal as possible. Multiresolution representation is an effective method for analyzing the information content of signals, and it also fits a wide spectrum of visual signalprocessing and visual communication applications. wavelet transform is one kind of multiresolution representations, and has found a wide variety of application in many aspects, including signal analysis, image coding, imageprocessing, computer vision and etc. Due to its characteristic of multiresolution signal decomposition, wavelet transform is used in this paper to do the image mosaic by choosing the width of mosaic transition zone proportional to the frequency represented in the band. Both 1-D and 2-D signal mosaics will be described, and some factors which affect the mosaics will be discussed.
A scheme for compression of rotation images is proposed in the paper. First, we fake a wavelet transform to decompose an image using a biorthogonal filter bank. Second, according to Shannon's rate distortion theor...
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ISBN:
(纸本)7505338900
A scheme for compression of rotation images is proposed in the paper. First, we fake a wavelet transform to decompose an image using a biorthogonal filter bank. Second, according to Shannon's rate distortion theory, an interband VQ is developed,with the exception that if gain is below a threshold, it is simply considered as a zero vector. Third, Haffman encoding is applied to vectors, and SQ is applied to the low passed signal at the coarsest level. Finally, rate-distortion performance test for rotation images and Lena image has been performed.
We study the problem of choosing an image based optimal wavelet basis with compact support for image data compression and provide a general algorithm for computing the optimal wavelet basis. We parameterize the mother...
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We study the problem of choosing an image based optimal wavelet basis with compact support for image data compression and provide a general algorithm for computing the optimal wavelet basis. We parameterize the mother wavelet and scaling function of wavelet systems through a set of real coefficients of the relevant quadrature mirror filter (QMF) banks. We further introduce decomposition entropy as an information measure to describe the distance between a given digital image and its projection into the subspace spanned by the wavelet basis. The optimal basis for the given image is obtained through minimizing this information measure. The resulting subspace is used for image analysis and synthesis. Experiments show improved compression ratios due to the application of the optimal wavelet basis and demonstrate the potential applications of our methodology in image compression. This method is also useful for constructing efficient wavelet based image coding systems.
As an approach to the wavelet detection of local scale and orientation in 2D images we make use of a well known, computationally efficient triangulation of the image domain and some of its lesser-known properties. We ...
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