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An improved variational level set method is proposed and applied to road extraction of high-resolution remote sensing images. The new model is a variational level set method which is adapted to extract objects of interest from complex background and is achieved by introducing three terms into GACV (Geodesic-Aided C-V) model The three terms are the target(More)
In this paper, we proposed AS<sup>3</sup>C-N algorithm, a method of adaptive semi-supervised spectral clustering based on Nystr&#x00F6;m approximation, and apply it to color image classification. Firstly, Introduction and analysis of spectral grouping using the Nystr&#x00F6;m method are given. compared with NJW spectral clustering, Nystr&#x00F6;m(More)
This paper proposes an algorithm for road extraction in high resolution remote sensing images based on mathematic morphology and snake model, which can divide up the area of objective directly from an image and combine with snake model to extract the road. Accordingly, the first step consists in extracting the road area from the high resolution remote(More)
In the era of big data, Content-Based Image Retrieval combined with deep learning technology gradually becomes the mainstream. This method can overcome some drawbacks of traditional CBIR, but at the same time there are still some problems to be solved, such as: The extracted feature dimension (generally more than 2000) is higher, which is not beneficial for(More)
The authors propose a variational level set image segmentation method for intensity inhomogeneous texture image. The method first extracts the main image structure by a relative total variation image decomposition method, which can better decompose the image into structural and textural parts. Then only uses the structural part as the input image for the(More)