The Experts below are selected from a list of 29160 Experts worldwide ranked by ideXlab platform
Amrane Houacine - One of the best experts on this subject based on the ideXlab platform.
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redundant versus orthogonal Wavelet Decomposition for multisensor image fusion
Pattern Recognition, 2003Co-Authors: Youcef Chibani, Amrane HouacineAbstract:Abstract The Wavelet Decomposition has become an attractive tool for fusing multisensor images. Usually, the input images are decomposed with an orthogonal Wavelet in order to extract features, which are combined through an appropriate fusion rule. The fused image is then reconstructed by applying the inverse Wavelet transform. In this paper, we investigate the use of the nonorthogonal (or redundant) Wavelet Decomposition as an alternative approach for feature extraction. By using test and remote sensing images, various fusion rules are considered and the detailed comparison indicates the superiority of this approach compared to the standard orthogonal Wavelet Decomposition for image fusion.
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the joint use of ihs transform and redundant Wavelet Decomposition for fusing multispectral and panchromatic images
International Journal of Remote Sensing, 2002Co-Authors: Youcef Chibani, Amrane HouacineAbstract:Intensity hue saturation (IHS) and Wavelet Decomposition are two distinct fusion methods used for enhancing the spatial resolution of multispectral images by exploiting a high-resolution panchromatic image. In this paper, a combination of the IHS transform and redundant Wavelet Decomposition is proposed as a general method for fusing multisensor images. The principle consists of transforming low-resolution multispectral images into IHS independent components. The low-resolution intensity component is fused with the high-resolution panchromatic image in the redundant Wavelet domain through an appropriate model. Subsequently, the high-resolution intensity produced is substituted to the low-resolution intensity. High spatial resolution multispectral images are then obtained through an inverse IHS transformation. SPOT images are used to illustrate the superiority of this approach over the IHS fuser in terms of preservation of spectral properties.
Z. Ling - One of the best experts on this subject based on the ideXlab platform.
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Texture segmentation using hierarchical Wavelet Decomposition
Pattern Recognition, 1995Co-Authors: Ezzatollah Salari, Z. LingAbstract:This paper presents a texture segmentation algorithm based on a hierarchical Wavelet Decomposition. Using Daubechies four-tap filter, an original image is decomposed into three detail images and one approximate image. The Decomposition can be recursively applied to the approximate image to generate a lower resolution of the pyramid. The segmentation starts at the lowest resolution using the K-means clustering scheme and textural features obtained from various sub-bands. The result of segmentation is propagated through the pyramid to a higher resolution with continuously improving the segmentation. The lower resolution levels help to build the contour of the segmented texture, while higher levels refine the process, and correct possible errors.
Skander Soltani - One of the best experts on this subject based on the ideXlab platform.
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On the use of the Wavelet Decomposition for time series prediction
Neurocomputing, 2002Co-Authors: Skander SoltaniAbstract:Abstract This paper deals with nonlinear time series prediction. The proposed method combines the Wavelet Decomposition (as a filtering step) and neural networks to provide an acceptable prediction value. Basically, the Wavelet Decomposition uses a pair of filters to decompose iteratively the original time series. It results in a hierarchy of new time series that are easier to model and predict. These filters must satisfy some constraints such as causality, information lossless, etc. We prove here that our method reduces the empirical risk comparatively to the classical ones. As an illustration, the results obtained on both sunspot and MacKey–Glass time series are shown.
Ias Sri Wahyuni - One of the best experts on this subject based on the ideXlab platform.
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Neighbour Alpha Stable Fusion in Wavelet Decomposition and Laplacian Pyramid
5th International Conference on Data Mining and Applications (DMAP 2019), 2019Co-Authors: Rachid Sabre, Ias Sri WahyuniAbstract:In this paper, a new multifocus image fusion method is proposed, which combines the Laplacian pyramid, Wavelet Decomposition and uses alpha stable distance as a selection rule. First, using Laplacian pyramid, we decomposed the multifocus images into several levels of pyramid and then applied Wavelet Decomposition at each level. The contribution of this work is to fuse the Wavelet images at each level by using alpha stable distance as a selection rule. To get the final fused image, we reconstructed the combined image at every level of the pyramid. This protocolwas compared to other methods and showed good results.
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Wavelet Decomposition in Laplacian Pyramid for Image Fusion
International Journal of Signal Processing Systems, 2016Co-Authors: Rachid Sabre, Ias Sri WahyuniAbstract:The aim of image fusion is to combine information from the set of images to get a single image which contains a more accurate description than any individual source image. While the scene contains objects in different focus due to the limited depth-of-focus of optical lenses in camera then by using image fusion technique we can get an image which has better focus across all area. In this paper, a multifocus image fusion method using combination Laplacian pyramid and Wavelet Decomposition is proposed. The fusion process contains the following steps: first, the multifocus images are decomposed using Laplacian pyramid into several levels of pyramid. Then at each level of pyramid, Wavelet Decomposition is applied. The images at every level of Wavelet are fused using maximum absolute value rule. The inverse Wavelet transform is then applied to the combined coefficients to produce the fused image in laplacian pyramid. The final step is to reconstruct the combined image at every level of pyramid to get the fused image which shows an image retaining the focus from the several input images. Experimental results that are quantitatively evaluated by calculation of root mean square error, peak signal to noise ratio, entropy, and average gradient measures for fused image show the proposed method can give good result.
Jiang Jia-fu - One of the best experts on this subject based on the ideXlab platform.
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Face Recognition Based on Frequency Spectrum of Wavelet Decomposition and AIS
Computer Simulation, 2004Co-Authors: Jiang Jia-fuAbstract:In this paper, a face recognition method using Wavelet Decomposition and AIS is presented. The energy of face image is concentrated on the lower sub-band after Wavelet Decomposition, and the feature of face image is extracted by analyzing frequency spectrum of Wavelet Decomposition which is not affected more by expression, besides, the storage and computing are reduced obviously. AIS is the simulation of NIS, and it can distinguish cells between self's and exterior's through revolution, and its clone arithmetic is good at keeping variety. When an image is input we can use the set of template feature which is made by pattern recognition of AIS to decide which class it is.
