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This paper systematically advocates a robust and efficient unsupervised multi-class co-segmentation approach by leveraging underlying subspace manifold propagation to exploit the cross-image coherency. It can combat certain image co-segmentation difficulties due to viewpoint change, partial occlusion, complex background, transient illumination, and cluttering texture patterns. Our key idea is to construct...
Interactive image segmentation is able to extract the user-specified foreground objects from the whole image, which remains to be a challenging problem in image processing and computer vision. The traditional pixel-based interactive segmentation is time-consuming and neglects the neighbor information, which is hard to achieve efficient and accurate results. To address this problem, a novel region-based...
The improvement of the quality of life brings people not only a lot of convenience, but also some bad habits which contribute to some fatal cardiovascular diseases. And it is also proved that the high fat content of tissues has a close relationship with some undesirable diseases, such as the Diabetes, Obesity, Hypertension and so forth. Current approaches to measure body fat content are limited and...
Despite the recent success of extensive co-segmentation studies, they still suffer from limitations in accommodating multiple-foreground, large-scale, high-variability image set, as well as their underlying capability for parallel implementation. To improve, this paper proposes a bi-harmonic distance governed flexible method for the robust coherent segmentation of the overlapping/similar contents...
Region-based image segmentation is an important preprocessing step for high-level computer vision tasks. This paper presents a novel approach to image partition into regions that reflect the objects in a scene. It explores the feasibility of utilizing Gray Level Co-occurrence Matrix (GLCM) and RIQ color feature of regions to improve the segmentation results produced by Recursive Shortest Spanning...
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