Contourlet

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

A hybrid NSCT domain image watermarking scheme

Abstract: At present, dealing with the piracy and tampering of images has become a notable challenge, due to the presence of smart mobile gadgets. In this paper, we propose a novel watermarking algorithm based on non-subsampled contourlet transform (NSCT) for improving the security aspects of such images. Moreover, the fusion of feature searching approach with watermarking methods has gained prominence in the current years. The scale-invariant feature transform (SIFT) is a technique in computer vision for detecting and illustrating the local features in images. Nevertheless, the SIFT algorithm can extract feature points with high invariance that are resilient to several issues like rotation, compression, and scaling. Furthermore, the extracted feature points are embedded with watermark using the NSCT approach. Subsequently, the tree split, voting, rotation searching, and morphology techniques are employed for improving the robustness against the noise. The proposed watermarking algorithm offers superior capability, better capture quality, and tampering resistance, when compared with existing watermarking approaches.

A survey of denoising techniques for multi-parametric prostate MRI

Abstract: Denoising is one of active area of research in the image-processing domain since last decade. Internal and external conditions of acquisition device are the main source of noise in an image during the procurement process, which is often impossible to avoid in practical situations. Since many different image denoising algorithms have been recommended till date, but the issue of noise elimination remains an undefended challenge. The main objective of this paper is to study and analyze the behavior of different denoising filters for multi-parametric (mp) prostate MRI so that the appropriate filter can be selected unanimously. This study evaluates the performance of fifteen denoising filters (Anisotropic, Median, Wiener, Gaussian, Mean, Wavelet, Contourlet, Bilateral, Curvelet, WHMT, NLM, GFOE, LMMSE, CURE-LET and ARF) w.r.t mp-prostate MRI i.e. T2w, DCE and DWI images in the presence of Gaussian and Rician noise. Evaluation is done in both variable and fixed level of noise. Both subjective and objective quality assessment parameters are considered for determining the final rating of filters executed over 300 mp-MRI images. This study concludes that anisotropic and NLM filter should be opted for denoising task because of their structural and other crucial details preserving capability.

An Efficient Expression Recognition Method using Contourlet Transform

Abstract: The paper proposes an efficient human expression recognition method in transformed domain using discrete Contourlet transform (DCT). DCT represents smooth contour information in different directions reflecting human perception and so relevant to recognize facial expressions more accurately. Each face is decomposed using discrete Contourlet transform up to fourth level and coefficients of high frequency and low frequency components with varied scales and angles are obtained. Logarithmic invariant moments of directional coefficients at different levels and histogram analysis are used to build the feature vectors for classification. To reduce dimension of the feature vectors, directional subbands are selected by analyzing the entropy of the feature vectors. Support Vector Machine (SVM) is applied to classify different expressions using the proposed method. Experimental results show promising performance applied on JAFFE and Cohn-Kanade database compare to other transformed domain methods.