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Alzheimer's disease (AD) is a leading cause of dementia, which causes serious health and socioeconomic problems. A progressive neurodegenerative disorder, Alzheimer's causes the structural change in the brain, thereby affecting behavior, cognition, emotions, and memory. Numerous multivariate analysis algorithms have been used for classifying AD, distinguishing it from healthy controls (HC). Efficient early classification of AD and mild cognitive impairment (MCI) from HC is imperative as early preventive care could help to mitigate risk factors. Magnetic resonance imaging (MRI), a noninvasive biomarker, displays morphometric differences and cerebral structural changes. A novel approach for distinguishing AD from HC using dual-tree complex wavelet transforms (DTCWT), principal coefficients from the transaxial slices of MRI images, linear discriminant analysis, and twin support vector machine is proposed here. The prediction accuracy of the proposed method yielded up to 92.65 ± 1.18 over the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset, with a specificity of 92.19 ± 1.56 and sensitivity of 93.11 ± 1.29, and 96.68 ± 1.44 over the Open Access Series of Imaging Studies (OASIS) dataset, with a sensitivity of 97.72 ± 2.34 and specificity of 95.61 ± 1.67. The accuracy, sensitivity, and specificity achieved using the proposed method are comparable or superior to those obtained by various conventional AD prediction methods.

Recently, the task of validating the authenticity of images and the localization of tampered regions has been actively studied. In this paper, we go one step further by providing solid evidence for image manipulation. If a certain image is proved to be the spliced image, we try to retrieve the original authentic images that were used to generate the spliced image. Especially for the image retrieval of spliced images, we propose a hybrid image-retrieval method exploiting Zernike moment and Scale Invariant Feature Transform (SIFT) features. Due to the symmetry and antisymmetry properties of the Zernike moment, the scaling invariant property of SIFT and their common rotation invariant property, the proposed hybrid image-retrieval method is efficient in matching regions with different manipulation operations. Our simulation shows that the proposed method significantly increases the retrieval accuracy of the spliced images.

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The three-dimensional (3D) extension of the high-efficiency video coding (3D-HEVC) standard adopts new depth-modelling modes (DMMs) to provide an alternative prediction scheme for depth-map intra-coding. In 3D-HEVC, although edges in depth maps can be accurately estimated by utilising the DMMs, testing the DMMs in the mode decision introduces a huge computational load to the encoder. A fast mode decision algorithm is proposed that can significantly reduce the computational overhead incurred by the DMMs. Experimental results indicate that the proposed algorithm reduces the encoding complexity by 33.89% on average without any penalty in coding efficiency.Introduction: The multiview video plus depth (MVD) format is one of the most popular approaches for three-dimensional (3D) video representation. In the MVD format, a small number of videos captured from different viewpoints and associated depth maps are compressed into a 3D video bitstream. After the video and depth data have been decoded, additional intermediate views can be synthesised using a depth-image-based rendering technique [1].The 3D extension of high-efficiency video coding (3D-HEVC) is the state-of-the-art video coding standard for efficient compression of MVD data. In the 3D-HEVC design, 35 intra-prediction modes of HEVC are utilised together with the newly added depth-modelling modes (DMMs) for depth-map intra-coding [2]. Table 1 shows the mode number m and associated name of each intra-prediction mode in 3D-HEVC. It was presented in [3] that testing the DMMs in the mode decision process introduces a huge computational load to the encoder. Therefore, the complexity reduction of the DMMs is an important research area of 3D-HEVC.