Contourlet

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

Robust contourlet-based blind watermarking for depth-image-based rendering 3D images

Abstract: The copyright protection for 3D multimedia has attracted considerable attention and depth-image-based rendering (DIBR) has been playing a critical role in 3D content representation due to its numerous advantages. In this paper, we propose a new blind watermarking scheme for DIBR 3D images. Considering the directional multiresolution image representation and convenient tree structures of contourlet transform (CT), we perform the watermark embedding and extraction in contourlet domain. The center view and the depth map are available at the content provider side. After applying contourlet transform to the center view, we embed the watermark into the selected contourlet subbands of the center view by quantization on certain contourlet coefficients. The virtual left and right views are generated from the watermarked center view and the associated depth map using DIBR technique at the receiver side. The statistical differences between quantized and unquantized contourlet coefficients are used for watermark extraction. The watermark can be detected with a low bit error rate (BER) from the center view, the left and right views even when each view is distorted and distributed separately. The simulation results demonstrate that the proposed scheme is very robust to image compression, noise addition and geometric attacks such as rotation, scaling and cropping. Moreover, the proposed scheme has good performance in terms of depth image variation and baseline distance adjustment. HighlightsA novel blind watermarking scheme for depth-image-based rendering 3D images is proposed.The watermark embedding and extraction are performed in contourlet domain.The watermark can be detected with a low bit error rate from the watermarked center view, and the synthesized left and right views even when each view is distorted and distributed separately.The proposed scheme is robust to geometric attacks and common DIBR processing.

Deep multimodal biometric recognition using contourlet derivative weighted rank fusion with human face, fingerprint and iris images

Abstract: The goal of multimodal biometric recognition system is to make a decision by identifying their physiological behavioural traits. Nevertheless, the decision-making process by biometric recognition s...

Remote Sensing Images Fusion Algorithm Based on Shearlet Transform

Abstract: The shearlet can give an asymptotic optimal representation of edges and contours in images by virtue of it characteristics of good multiresolution and high directionality. In this paper, a novel image fusion strategy is presented for panchromatic high resolution images and multispectral images in shearlet transform domain. The fusion algorithm use the intensity component addition strategy based on LHS transform to preserve spatial resolution and color content. The experimental results demonstrate that the proposed algorithm, compared to intensity-hue-saturation (IHS) transform technique, corresponding wavelet transform-based fusion method, and contourlet transform-based fusion method, can improve spatial resolution and keep spectral information. In addition, the proposed algorithm can extract more useful information from source images, and make the fused image with higher performance in terms of both visual quality and objective evaluation criteria.

An effective similarity measure via genetic algorithm for Content-Based Image Retrieval with extensive features

Abstract: With the aid of image content, the relevant images can be extracted from the image in the Content Based Image Retrieval (CBIR) system. Concise feature sets limit the retrieval efficiency, to eliminate this shape, colour, texture and contourlet features are extracted. For retrieving relevant images, the optimisation technique Genetic Algorithm (GA) is utilised and for similarity measure Squared Euclidean Distance (SED) is utilised for comparing query image featureset and database image featureset. Hence, from GA based similarity measure, relevant images are retrieved and evaluated by querying different images.