Xianbin Wen

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After Mountcastle proposed the theory that all parts of mammalian neocortex are uniform [1], more and more evidences were found to prove that no matter what function the field of neocortex provide, the organizing of cortex cells are same and have an hierarchical structure. It can be inferred every unit of neocortex process information using an identical(More)
A new algorithm in Exemplar-Based image completion is proposed using color Ratio Gradient. Color Ratio Gradient Histogram is robust to object occlusion and clustering which induce mismatch in general image match algorithm. The proposed algorithm can improve the quality of comparing similarity between source image patch and target image patch, then improve(More)
This paper presents a new concept on characterizing the similarity between nodes of a weighted undirected graph with application to multiscale spectral clustering. The contribution may be divided into three parts. First, the generalized mean first-passage time (GMFPT) and the generalized mean recurrence time (GMRT) are proposed based on the multi-step(More)
A new spectrum sensing scheme based on spatial-temporal opportunity detection is proposed in this paper. To verify the practicability and validity of the method, we proposed a more real 2-D cognitive wireless sensor network (CWSN) topology model, where the primary signal transmitter is distributed in a given sensing area randomly at each iteration. The(More)
Aiming at the problem that how to improve the accuracy of image registration, this paper presents an approach for image registration based on mutual information and non-subsampled contourlet transform. First of all, the reference image and the floating image are decomposed with nonsubsampled contourlet transform. Secondly, register approximate component of(More)
We propose a novel, robust and multiscale despeckling method, based on the robust multiscale scale-recursive estimation algorithm of multiscale autoregressive (MAR) model on dyadic tree. First, a suitable MAR model, which provide a powerful framework for describing random process and fields that evolve in scale, is selected to model SAR image. Then,(More)
A multi-layer classification approach based on multi-scales and multi-features (ML–MFM) for synthetic aperture radar (SAR) images is proposed in this paper. Firstly, the SAR image is partitioned into superpixels, which are local, coherent regions that preserve most of the characteristics necessary for extracting image information. Following this, a new(More)