Contourlet

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

A novel method using contourlet to extract features for iris recognition system

Abstract: In different areas of Biometrics, recognition by iris images in nowadays has been taken into consideration by researchers as one of the common methods of identification like passwords, credit cards or keys. Iris recognition a novel biometric technology has great advantages such as variability, stability and security. Although the area of the iris is small it has enormous pattern variability which makes it unique for every one and hence leads to high reliability. In this paper we propose a new feature extraction method for iris recognition based on contourlet transform. Contourlet transform captures the intrinsic geometrical structures of iris image. It decomposes the iris image into a set of directional sub-bands with texture details captured in different orientations at various scales so for reducing the feature vector dimensions we use the method for extract only significant bit and information from normalized iris images. In this method we ignore fragile bits. At last, the feature vector is created by using Co-occurrence matrix properties. For analyzing the desired performance of our proposed method, we use the CASIA dataset, which is comprised of 108 classes with 7 images in each class and each class represented a person. And finally we use SVM and KNN classifier for approximating the amount of people identification in our proposed system. Experimental results show that the proposed increase the classification accuracy and also the iris feature vector length is much smaller versus the other methods.

Multiscale sparse representation of high-resolution computed tomography (HRCT) lung images for diffuse lung disease classification

Abstract: A multiscale sparse representation scheme based on wavelet and contourlet transforms is employed to describe four patterns of diffuse lung disease patterns: normal, emphysema, ground glass opacity (GGO) and honey-combing based on HRCT lung images. First, using sparse representation, four discriminative dictionaries are trained for the four patterns respectively. After that, in the classification phase, a patch or ROI is assigned to the pattern with minimum resconstruction error. The method is tested on a collection of 89 slices from 38 patients, each slice of size 512×512, 16 bits/pixel in DICOM format. The dataset contains 73,000 ROIs of those slices marked by experienced radiologists. We employ this technique with 2-scale wavelet and [2 3] contourlet transform for diffuse lung disease classification. The technique presented here has the overall sensitivity of 91.05% and specificity 97.01%.

Contourlet domain SAR image de-speckling via self-snake diffusion and sparse representation

Abstract: In this paper, A contourlet domain SAR image de-speckling algorithm via self-snake diffusion and sparse representation theory is presented in order to reduce the influence of the SAR image speckle noise on the large-scale target edge information of the low frequency subband and the texture information of the high frequency subband. For this algorithm, firstly, the contourlet transform is applied to the speckled SAR image, adjusts the directional number of each dimension to represent SAR image in the high dimensional space. Then, the low frequency subband without sparsity is filtered by self-snake diffusion and the filtered coefficient is regarded as the local average estimate of the low-frequency subband in the contourlet domain. Sparse representation optimization model of SAR image is presented for suppressing the speckle noise of the high frequency subbands with sparsity, and solves sparse coefficients of the high frequency subbands by using the improved orthogonal matching pursuit algorithm. Finally, the de-speckled image is reconstructed from all of the filtered subband coefficients by the inverse contourlet transform. This paper simulates three representative experiments and the experimental results demonstrate that the proposed algorithm has a better de-speckling performance with preserving the edge of the SAR image.