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Keywords: Active contours Geodesic active contours Chan–Vese model Image segmentation Level set method a b s t r a c t A novel region-based active contour model (ACM) is proposed in this paper. It is implemented with a special processing named Selective Binary and Gaussian Filtering Regularized Level Set (SBGFRLS) method, which first selectively penalizes… (More)
Keywords: Image segmentation Chan–Vese (C–V) model Active contour models LBF model a b s t r a c t A new region-based active contour model that embeds the image local information is proposed in this paper. By introducing the local image fitting (LIF) energy to extract the local image information, our model is able to segment images with intensity… (More)
This paper presents a novel reaction-diffusion (RD) method for implicit active contours that is completely free of the costly reinitialization procedure in level set evolution (LSE). A diffusion term is introduced into LSE, resulting in an RD-LSE equation, from which a piecewise constant solution can be derived. In order to obtain a stable numerical… (More)
Some Experimental Results in our paper: 1. Segmentation of example images with weak edges (a) δ1,ρ(φ) is used. Results by our RD method .
—To take advantage of the wide swath width of Land-sat Thematic Mapper (TM)/Enhanced Thematic Mapper Plus (ETM+) images and the high spatial resolution of Système Pour l'Observation de la Terre 5 (SPOT5) images, we present a learning-based super-resolution method to fuse these two data types. The fused images are expected to be characterized by the swath… (More)
— In this paper, we present a novel spatial and spectral fusion model (SASFM) that uses sparse matrix factorization to fuse remote sensing imagery with different spatial and spectral properties. By combining the spectral information from sensors with low spatial resolution (LSaR) but high spectral resolution (HSeR) (hereafter called HSeR sensors), with the… (More)
on two types of data: images primarily with phenology change and images primarily with land-cover type change. Experimental results demonstrate the superiority of SPSTFM in capturing surface reflectance changes on both categories of images.
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