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Image segmentation can be viewed as the problem of topic assignment, in which pixels are grouped into regions with respect to the topic model of textures and real-world semantic meanings. In this paper, we apply a novel hierarchical topic model, which builds a multi-level image representation covers texture and semantic region, to image segmentation. Specifically, the proposed method firstly assigns...
Textures of land covers provide significant evidences for segmentation and classification. Inspired by resent researches on topic model, we work on a novel texture segmentation method for very high resolution (VHR) remote sensing images based on Latent Dirichlet Allocation (LDA). In order to model spatial relationship between words in LDA, a constraint random variable which is used to control the...
This paper parallelizes and characterizes an important computer vision application -Scale Invariant Feature Transform (SIFT) both on a Symmetric Multiprocessor (SMP) platform and a large scale Chip Multiprocessor (CMP) simulator. SIFT is an approach for extracting distinctive invariant features from images and has been widely applied. In many computer vision problems, a real-time or even super-real-time...
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