Dyadic wavelet transform has been used to derive affine invariant functions. The invariant functions are based on the dyadic wavelet transform of the object boundary. Two invariant functions have been calculated using...
详细信息
ISBN:
(纸本)0819437646
Dyadic wavelet transform has been used to derive affine invariant functions. The invariant functions are based on the dyadic wavelet transform of the object boundary. Two invariant functions have been calculated using different numbers of dyadic levels. Experimental results show that these invariant functions outperform some traditional invariant functions. The stability of these invariant functions have been tested for a large perspective transformation.
A perspective invariant function has been derived. It is based on analyzing the object boundary using the dyadic wavelet transform. Five dyadic wavelet transform levels are used to define the invariant function. The s...
详细信息
ISBN:
(纸本)0819437646
A perspective invariant function has been derived. It is based on analyzing the object boundary using the dyadic wavelet transform. Five dyadic wavelet transform levels are used to define the invariant function. The selection of the dyadic levels is discussed. Experimental results test the recognition power of the proposed invariant function. In addition: the stability of the invariant function is examined.
We introduce a pose estimation method for synthetic aperture radar (SAR) imagery using the 2-D continuous wavelet transform (CWT). The computational complexity of the new approach is comparable to other image-based ap...
详细信息
ISBN:
(纸本)0819437646
We introduce a pose estimation method for synthetic aperture radar (SAR) imagery using the 2-D continuous wavelet transform (CWT). The computational complexity of the new approach is comparable to other image-based approaches such as ones that incorporate principle component analysis (PCA). Using the public domain MSTAR database, we show that the CWT-based method provides a better pose estimate than the PCA method.
We describe a formalism that allows us to study space (or time)-scale correlations in multiscale processes. This method, based on the continuous wavelet transform, is particularly well suited to study multiplicative r...
详细信息
ISBN:
(纸本)0819437646
We describe a formalism that allows us to study space (or time)-scale correlations in multiscale processes. This method, based on the continuous wavelet transform, is particularly well suited to study multiplicative random cascades for which the correlation functions take very simple expressions. This two-point space-scale statistical analysis is illustrated on synthetic multifractal signals and then applied to financial time series and fully developed turbulence data.
The gyrator transform is a linear canonical transform, which generates the rotation of an optical signal in position-spatial frequency planes. Gyrator wavelet transform is a relatively newer optical information proces...
详细信息
The gyrator transform is a linear canonical transform, which generates the rotation of an optical signal in position-spatial frequency planes. Gyrator wavelet transform is a relatively newer optical information processing tool obtained by combining the gyrator transform with the wavelet transform. This combination provides multi-resolution analysis of an image which is twisted in spatial frequency planes. The proposed tool satisfies basic algebraic properties, such as the linearity property and Parseval's theorem. Considering the usefulness of this tool, here a study of features, applications, and implementation of the gyrator wavelet transform is presented. This work studies the features of the gyrator wavelet transform, which can find a role in different applications such as edge enhancement, image encryption, image hiding, and watermarking.
Fractographs are elevation maps of the fracture zone of some broken material. The technique employed to create these maps (confocal laser microscopy) often introduces noise composed of positive or negative 'spikes...
详细信息
ISBN:
(纸本)0819437646
Fractographs are elevation maps of the fracture zone of some broken material. The technique employed to create these maps (confocal laser microscopy) often introduces noise composed of positive or negative 'spikes' that must be removed before further analysis. Since the roughness of these maps contains useful information, it must be preserved. Consequently, conventional denoising techniques cannot be employed. We use continuous and discrete wavelet transforms of these images, and the properties of wavelet coefficients related to pointwise Holder regularity, to detect and remove the spikes.
Poor directional selectivity, a major disadvantage of the separable 2D discrete wavelet transform (DWT), has previously been circumvented either by using highly redundant, nonseparable wavelet transforms or by using r...
详细信息
ISBN:
(纸本)0819437646
Poor directional selectivity, a major disadvantage of the separable 2D discrete wavelet transform (DWT), has previously been circumvented either by using highly redundant, nonseparable wavelet transforms or by using restrictive designs to obtain a pair of wavelet. trees. In this paper, we demonstrate that superior directional selectivity may be obtained with no redundancy in any separable wavelet transform. We achieve this by projecting the wavelet coefficients to separate approximately the positive and negative frequencies. Subsequent decimation maintains nonredundancy. A novel reconstruction step guarantees perfect reconstruction within this critically-sampled framework. Although our transform generates complex-valued coefficients, it may be implemented with a fast algorithm that uses only real arithmetic. We also explain how redundancy may be judiciously introduced into our transform to benefit certain applications. To demonstrate the efficacy of our projection technique, we show that it achieves state-of-the-art performance in a seismic image-processing application.
We present statistics on natural images dealing with homogeneous and connected regions. defined as connected components of differences of level sets. We show that their area is distributed according to a power law. Fr...
详细信息
ISBN:
(纸本)0819437646
We present statistics on natural images dealing with homogeneous and connected regions. defined as connected components of differences of level sets. We show that their area is distributed according to a power law. From these experimental results, we show precisely how the modeling of images as functions of bounded variation, widely used in image restoration methods, is incompletely adapted to natural images. For this purpose we use a model of convolution and sampling for the formation of images. We also use our experimental results to confirm the scale invariance of natural images.
We present methods used to measure the information in anastronomical image, in both a statistical and a deterministic way. Wediscuss the wavelet transform and noise modeling, and describe how tomeasure the information...
详细信息
We present methods used to measure the information in anastronomical image, in both a statistical and a deterministic way. Wediscuss the wavelet transform and noise modeling, and describe how tomeasure the information and the implications for object detection,filtering, and deconvolution. The perspectives opened up by the range ofnoise models, catering for a wide range of eventualities in physicalscience imagery and signals, and the new two-pronged but tightly coupledunderstanding of the concept of information have given rise to betterquality results in applications such as noise filtering, deconvolution,compression, and object (feature) detection. We have illustrated some ofthese new results in this article. The theoretical foundations of ourperspectives have been sketched out. The practical implications, too,are evident from the range of important signalprocessing problems whichwe can better address with this armoury of methods. The resultsdescribed in this work are targeted at information and at *** we have focused on experimental results in astronomical image andsignalprocessing, the possibilities are apparent in many otherapplication domains
In this paper, we propose a statistical modeling of images based on a decomposition with complex-valued Daubechies wavelets. These wavelets possess interesting properties that can be turn into account in the modeling ...
详细信息
ISBN:
(纸本)0819437646
In this paper, we propose a statistical modeling of images based on a decomposition with complex-valued Daubechies wavelets. These wavelets possess interesting properties that can be turn into account in the modeling to obtain a better characterization of the images. This characterization is achieved by statistically modeling the wavelet coefficient distribution via a hidden Markov tree model. The wavelet coefficients in an image are organized into three tree structures and this type of model has already been used successfully in this context by independently modeling each of these trees. We propose a further refinement by considering the joint modeling of the three trees with a so-called mixed memory hidden Markov tree model. The model is based on a memory mixture, a general approach to obtain an approximation of the joint distribution in the presence of factorial Markov models. The utilization of such model is quite general and can be applied to various signal-processing problems. To illustrate the interest of this model as well as the relevance of using complex Daubechies wavelets, we evaluate their performance for a classification and a denoising application.
暂无评论