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In this paper, we present a multiview approach to segment the foreground objects consisting of a group of people into individual human objects and track them across the video sequence. Depth and occlusion information recovered from multiple views of the scene is integrated into the object detection, segmentation, and tracking processes. Adaptive background penalty with occlusion reasoning is proposed to separate the foreground regions from the background in the initial frame. Multiple cues are employed to segment individual human objects from the group. To propagate the segmentation through video, each object region is independently tracked by motion compensation and uncertainty refinement, and the motion occlusion is tackled as layer transition. The experimental results implemented on both our sequences and other's sequence have demonstrated the algorithm's efficiency in terms of subjective performance. Objective comparison with a state-of-the-art algorithm validates the superior performance of our method quantitatively. [ABSTRACT FROM PUBLISHER]

Efficient image watermarking calls for full exploitation of the perceptual distortion constraint. Second-order statistics of visual stimuli are regarded as critical features for perception. This paper proposes a second-order statistics (SOS)-based image quality metric, which considers the texture masking effect and the contrast sensitivity in Karhunen–Loève transform domain. Compared with the state-of-the-art metrics, the quality prediction by SOS better correlates with several subjectively rated image databases, in which the images are impaired by the typical coding and watermarking artifacts. With the explicit metric definition, spread spectrum watermarking is posed as an optimization problem: we search for a watermark to minimize the distortion of the watermarked image and to maximize the correlation between the watermark pattern and the spread spectrum carrier. The simple metric guarantees the optimal watermark a closed-form solution and a fast implementation. The experiments show that the proposed watermarking scheme can take full advantage of the distortion constraint and improve the robustness in return. [ABSTRACT FROM PUBLISHER]

Because of the outstanding contribution in improving compression efficiency, block-based quantization has been widely accepted in state-of-the-art image/video coding standards. However, false contour artifacts are introduced, which result in reducing the fidelity of the decoded image/video especially in terms of subjective quality. In this paper, a block-based decontouring method is proposed to reduce the false contour artifacts in the decoded image/video by automatically dithering its direct current (DC) value according to a composite model established between gradient smoothness and block-edge smoothness. Feature points on the model with the corresponding criteria in suppressing contour artifacts are compared to show a good consistency between the model and the actual processing effects. Discrete cosine transform (DCT)-based block level contour artifacts detection mechanism ensures the blocks within the texture region are not affected by the DC dithering. Both the implementation method and the algorithm complexity are analyzed to present the feasibility in integrating the proposed method into an existing video decoder on an embedded platform or system-on-chip (SoC). Experimental results demonstrate the effectiveness of the proposed method both in terms of subjective quality and processing complexity in comparison with the previous methods. [ABSTRACT FROM PUBLISHER]

Most existing feature detectors assume no surface discontinuity within the keypoints' support regions and, hence, have little chance to match the keypoints located on or near the surface boundaries. These keypoints, though not many, are salient and representative. In this paper, we show that they can be successfully matched by using the proposed scale- and affine-invariant Fan features. Specifically, the image neighborhood of a keypoint is depicted by multiple fan subregions, namely Fan features, to provide robustness to surface discontinuity and background change. These Fan features are made scale-invariant by using the automatic scale selection method based on the Fan Laplacian of Gaussian (FLOG). Affine invariance is further introduced to the Fan features based on the affine shape diagnosis of the mirror-predicted surface patch. The Fan features are then described by Fan-SIFT, which is an extension of the famous scale-invariant feature transform (SIFT) descriptor. Experimental results of quantitative comparisons show that the proposed Fan feature has good repeatability that is comparable to the state-of-the-art features for general structured scenes. Moreover, by using Fan features, we can successfully match image structures near surface discontinuities despite significant scale, viewpoint, and background changes. These structures are complementary to those found by the traditional methods and are especially useful for describing weakly textured scenes, which is demonstrated in our experiments on image matching and object rendering. [ABSTRACT FROM AUTHOR]