Combined Contourlet and Non-subsampled Contourlet Transforms Based Approach for Personal Identification Using Palmprint

Masood, Hassan; Asim, Mohammad; Mumtaz, Muhammad Zubair; Mansoor, Asif

doi:10.1109/dicta.2009.73

Cited by 13 publications

(3 citation statements)

References 21 publications

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“…Three common filter banks used in NSDFB are sinc (ideal filter), pkva(ladder filters) and maxflat(diamond maxflat filters obtained from a three stage ladder) [4]. According to the frequency partition characteristics of them and real-time requirement of the defect detection algorithm, a new NSDFB based on 9/7 wavelet filter banks with rational coefficients [5], which is denoted by 9-7 below, are given in this paper.…”

Section: Fig 4 Eight Directional Filter Banks Based On the Fan Filtmentioning

confidence: 99%

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Research on fabric defect detection via different filters combination in NSCT

Zhang

Ren

2014

2014 12th International Conference on Signal Processing (ICSP)

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A new kind of NonSubsampled Contourlet Transform (NSCT) based on 9/7wavelet filter banks with rational coefficients is given in this paper. And four kinds of NSCT methods, one of which is this new NSCT, are applied to a new fabric defect detection algorithm respectively. In the algorithm, the fabric defect image is firstly decomposed into different frequency subbands by NSCT. Secondly, both the low frequency subband and the optimal high fequency subbands selected from all high frequency subbands by using a cost function are thresholded. Finally, these thresholded subbands are fused and then binarized in order to separate the defect from the image texture background. The contrast experiment results of four kinds of NSCT methods show that this new NSCT has good performance both in defect detection effect and in algorithm's execution time.Index Terms-Fabric defect detection, NonSubsampled Contourlet Transform, 9/7wavelet filter banks with rational coefficients, subband fusion, image segmentation

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Section: Fig 4 Eight Directional Filter Banks Based On the Fan Filtmentioning

confidence: 99%

“…Calculate the mean j mh and the standard deviation j stdh of the four optimal high frequency subbands using the formulae given by (3) and (4). And then, calculate the deviation image according to (10):…”

Section: ( )mentioning

confidence: 99%

Research on fabric defect detection via different filters combination in NSCT

Zhang

Ren

2014

2014 12th International Conference on Signal Processing (ICSP)

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“…Hassan Masood等人 [4]提出结合Contourlet变换算法和非采样Contourlet变换算法利用掌纹进行模式识别等等。因其多尺度，多方向及时移不变的优点，本文将非采样方向小波用来对掌纹图像的预处理变换中，获取掌纹图像的细节信息和方向信息。核方法是当前模式识别领域中一个迅猛发展的新方向。其基本思想是引入一种非线性映射，把训练样本从输入空间映射到某一高维(甚至是无穷维)的特征空间中，在这高维特征空间中，样本可看成是线性可分的。 1998年，Scholkopf等人将核方法应用于特征抽取中，提出了核主成分分析法 (Kernel Principe Components analysis ，KPCA) [12]。Mika、Baudat和Anouar等提出用核函数实现非线性鉴别分析即核 Fisher 鉴别分析 (KFDA) [13]。在本文中，我们利用核技术将变换后的掌纹图像进行非线性映射，在这个高维特征空间中，对新的掌纹变换图像用线性鉴别技术，如 [14][15] …”

Section: 向小波变换用于图像水印技术的系统的设计和应用，unclassified