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Contourlet

About: Contourlet is a research topic. Over the lifetime, 3533 publications have been published within this topic receiving 38980 citations.


Papers
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Proceedings ArticleDOI
28 Jul 2010
TL;DR: In this paper two techniques for palmprint recognition are suggested and compared and it was found that the best achieved recognition rate is about 94% when combining the results of both techniques using the CT.
Abstract: In this paper two techniques for palmprint recognition are suggested and compared. Palmprint include principal lines, wrinkles and ridges which contain very important features essential for recognition. The Contourlet Transform (CT) is a multiresolution and multidirection transform which can be effective in capturing the palm features. The first technique extracts the edges from the palm images and then performs the CT or the Discrete Wavelet Transform (DWT) on the edge extracted images. The sub-band images are divided into M*M non-overlapping blocks. The energy of each block is calculated and normalized to form a feature vector. The second technique employs the principal component analysis PCA where the approximation images are input to it for dimensionality reduction and to produce the eigen palms. Features extracted from both techniques are tested and compared where it was found that the best achieved recognition rate is about 94% when combining the results of both techniques using the CT.

12 citations

Patent
01 Dec 2010
TL;DR: In this article, an SAR (Synthetic Aperture Radar) image de-noising method based on NSCT (Non-Sub-sampled Contourlet Transform) domain edge detection and a Bishrink model is proposed.
Abstract: The invention discloses an SAR (Synthetic Aperture Radar) image de-noising method based on NSCT (Non-Subsampled Contourlet Transform) domain edge detection and a Bishrink model, mainly solving the problems of scratch effect and detail loss caused by carrying out the SAR image de-noising process by means of non-subsampled contourlet transform The method comprises the following steps of: carrying out the non-subsampled contourlet transform on a selected SAR image and dividing the image into six layers of sub-band coefficients; keeping the first layer and the second layer of sub-band coefficients invariable and contracting the third to six layers of the sub-band coefficients by using the Bishrink model; reconstructing an image by means of non-subsampled contourlet inverse transform, and detecting an edge of the reconstructed image to carry out mean value filtering on the image subjected to the edge detection to obtain a filtered image; and carrying out non-linear anisotropism dispersion on a difference image obtained by subtracting the input image from the filtered image to obtain a de-noised image The invention can excellently maintain edge information of the image and point target characteristic information, and can be used for interpretation analysis in the SAR image and pre-processing of image understand

12 citations

Journal ArticleDOI
TL;DR: In this article, the authors proposed a new fusion scheme by combining principal component analysis (PCA) and non-sub-sampled contourlet transform (NSCT) to find a compromise between enhancing the spatial resolution and preserving the spectral information.
Abstract: The Pansharpening process aims to merge the high spatial resolution of the panchromatic (Pan) image with the spectral information of the multispectral (MS) images. The fused images should represent an enhanced spatial resolution and should preserve the spectral information simultaneously. In the two last decades, many pansharpening algorithms have been implemented in the literature such as IHS, PCA, HPF, etc. Therefore, in comparison with the various conventional methods, our contribution is the conception of a new fusion scheme by combining two different approaches: the Principal Component Analysis (PCA) and the NonSubsampled Contourlet Transform (NSCT). The hypothesis in this combination represent the use of PCA, in first, like statistical approach to obtain from the MS bands the main information, followed by the NSCT as a robust multiresolution and multidirectional approach, to give an optimal representation of the characteristics in the image compared to the classical methods (wavelets), in order to overcome the drawback caused by PCA with the spectral distortion. The focus of this study is to show a new way to combine differently from usual those two approaches, to find a compromise between enhancing the spatial resolution and preserving the spectral information at the same time. The quality of the resulted images has been evaluated by the visual interpretation and the statistical assessment to prove its efficiency compared to other conventional methods.

12 citations

Journal Article
TL;DR: A synergistic combination of segmentation techniques and binary particle swarm optimization (BPSO) intelligent search strategies is employed in salience analysis of contrast feature-vision system and results show that the BPSO algorithm is much faster than the traditional genetic algorithm.
Abstract: In this paper, an optimal and intelligent multi-focus image fusion algorithm is presented, expected to achieve perfect reconstruction or optimal fusion of multi-focus images with high speed. A synergistic combination of segmentation techniques and binary particle swarm optimization (BPSO) intelligent search strategies is employed in salience analysis of contrast feature-vision system. Also, several evaluations concerning image definition are exploited and used to evaluate the performance of the method proposed. Experiments are performed on a large number of images and the results show that the BPSO algorithm is much faster than the traditional genetic algorithm. The method proposed is also compared with some classical or new fusion methods, such as discrete wavelet-based transform (DWT), nonsubsampled contourlet transform (NSCT), NSCT-PCNN (pulse coupled neural networks (PCNN) method in NSCT domain) and curvelet transform. The simulation results with high accuracy and high speed prove the superiority and effectiveness of the present method.

11 citations

Proceedings ArticleDOI
18 Dec 2006
TL;DR: A novel watermarking algorithm proposed in Contourlet domain with ICA (independent component analysis) is proposed, which is robust to JPEG compression and noise attack, and especially robust to geometry attack, filter and image processing.
Abstract: In this paper, a novel watermarking algorithm is proposed in Contourlet domain with ICA (independent component analysis). Contourlet transform is adopted in watermarking embedding scheme. In contrast to the wavelet transform, a kind of contour segment base are adopted to approach singular curve, and a flexible multiresolution, local, and directional expansion of an image is obtained. Watermarking image is embedded into the most distinct subband in order to obtain robustness. ICA processing is adopted in watermarking detected scheme. The watermarking can be correctly detected by no care about the attack type and attack parameter which the watermarked image would be suffered. To prove the validity of our approach, we give experimental results, our algorithm is robust to JPEG compression and noise attack, and especially robust to geometry attack, filter and image processing.

11 citations


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Performance
Metrics
No. of papers in the topic in previous years
YearPapers
202336
202299
202175
2020109
2019155
2018164