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Superpixel based methods have recently shown success in scene segmentation and labeling. In scene labeling, a superpixel algorithm is used first to segment the image into visually consistent small regions; then several feature descriptors are computed and classification is performed for each superpixel. In this paper, Kernel Codebook Encoding (KCB) of superpixel features is proposed. In KCB feature...
Brain tumor segmentation, an essential but challenging task, has long attracted much attention from the medical imaging community. Recently, successful applications of sparse coding and dictionary learning has emerged in various vision problems including image segmentation. In this paper, a superpixel-based framework for automated brain tumor segmentation is introduced. The kernel trick is adopted...
In this paper, we introduce a novel approach for video compression that explores spatial as well as temporal redundancies over sequences of many frames in a unified framework. Our approach supports “compressed domain vision” capabilities. To this end, we developed a sparse Steered Mixture-of-Experts (SMoE) regression network for coding video in the pixel domain. This approach drastically departs from...
The kernel density estimation (KDE)-based image segmentation algorithm has excellent segmentation performance. However, this algorithm is computational intensive. In addition, although this algorithm can tolerant noise in the input images, such as the noise due to snow, rain, or camera shaking, it is sensitive to the noise from the internal computing circuits, such as the noise due to soft errors...
A regularized method to incorporate prior knowledge into spectral clustering in the form of pairwise constraints is proposed. This method is based on a weighted kernel principal component analysis (PCA) interpretation of spectral clustering with primal-dual least squares support vector machines (LS-SVM) formulations. The weighted kernel PCA framework allows incorporating pairwise constraints into...
Segmentation involves separating an object from the background in a given image. The use of image information alone often leads to poor segmentation results due to the presence of noise, clutter or occlusion. The introduction of shape priors in the geometric active contour (GAC) framework has proved to be an effective way to ameliorate some of these problems. In this work, we propose a novel segmentation...
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