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A novel contour-constrained superpixel (CCS) algorithm is proposed in this work. We initialize superpixels and regions in a regular grid and then refine the superpixel label of each region hierarchically from block to pixel levels. To make superpixel boundaries compatible with object contours, we propose the notion of contour pattern matching and formulate an objective function including the contour...
In this paper, we propose a fast deep learning method for object saliency detection using convolutional neural networks. In our approach, we use a gradient descent method to iteratively modify the input images based on the pixel-wise gradients to reduce a pre-defined cost function, which is defined to measure the class-specific objectness and clamp the class-irrelevant outputs to maintain image background...
Image matting refers to the problem of accurately extracting foreground objects in images and video. The most recent works in natural image matting relies on the local and manifold smoothness assumptions on foreground and background colors on which a cost function is established. In this paper, we present a framework of formulating new regularization for robust solutions and illustrate new algorithms...
We investigate the problem of finding minimum-distortion policies for streaming delay-sensitive but distortion-tolerant data. We consider cross-layer approaches which exploit the coupling between presentation and transport layers. We make the natural assumption that the distortion function is convex and decreasing. We focus on a single source-destination pair and analytically find the optimum transmission...
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