Topic
Contourlet
About: Contourlet is a research topic. Over the lifetime, 3533 publications have been published within this topic receiving 38980 citations.
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TL;DR: Experimental results show that the proposed feature extraction method improves recognition accuracy compare to other methods and efficiently handle the effect of Gaussian noise as tested on JAFFE, ORL and FERET database.
31 citations
TL;DR: This paper proposes a defect extraction method for magnetic tile images based on the shearlet transform, which outperforms the other methods considered and can very effectively extract defects.
31 citations
TL;DR: The proposed fusion algorithm based on neighborhood characteristic and regionalization in NSCT (Nonsubsampled Contourlet Transform) domain can leave enough information in the original images and its details, and the fused images have better visual effects.
31 citations
TL;DR: This study presents an ‘inversion attack’ resilient zero-watermarking system, in the hybrid Contourlet transform – singular value decomposition domain for medical image authentication, that preserves the fidelity of the host image without introducing any artefacts and employs triangular number generating function and Hu's image invariants to confront ‘ inversion attacks’.
Abstract: Medical images are watermarked with patient data to enforce patient authentication and identification in radiology practices. In addition to common threats such as signal processing and geometric attacks, medical image watermarking systems are susceptible to a new class of threats called ‘inversion attack’, leading to ambiguities in establishing rightful ownership. This study presents an ‘inversion attack’ resilient zero-watermarking system, in the hybrid Contourlet transform – singular value decomposition domain for medical image authentication. This scheme preserves the fidelity of the host image without introducing any artefacts and employs triangular number generating function and Hu's image invariants to confront ‘inversion attacks’. The performance of the system is evaluated with medical images of different modalities and a quick response code watermark that contains patient data. The experimental results demonstrate the robustness of the system against ‘ambiguity attacks’ and signify its appropriateness for secured medical image exchange between remote radiologists.
31 citations
01 Nov 2007
TL;DR: In this paper, the authors defined region energy and cousin correlation to represent the neighbors and cousins information, respectively, to obtain fused coefficients in the high-frequency NSCT domain, and then fused image is reconstructed by inverse NSCT.
Abstract: Nonsubsampled contourlet transform (NSCT) provides flexible multiresolution, anisotropy and directional expansion for images. Compared with the foremost contourlet transform, it is shift-invariant and can overcome the pseudo-Gibbs phenomena around singularities. In addition, coefficients of NSCT are dependent on their neighborhood coefficients in the local window and cousin coefficients in directional subbands. In this paper, region energy and cousin correlation are defined to represent the neighbors and cousins information, respectively. Salience measure, as the combination of region energy and cousin correlation, is defined to obtain fused coefficients in the high-frequency NSCT domain. First, source images are decomposed into subimages via NSCT. Secondly, salience measure is computed. Thirdly, salience measure-maximum-based rule and average rule are employed to obtain high-frequency and low-frequency coefficients, respectively. Finally, fused image is reconstructed by inverse NSCT. Experimental results show that the proposed algorithm outperforms wavelet-based fusion algorithms and contourlet transform-based fusion algorithms.
31 citations