This paper proposed a new multiscale and multidirectional image representation method named CBlet transform. It combines the new contourlet transform with the second-generation bandeletzation procedure. Thereinto, the contourlet transform captures image discontinuous points and links them into linear structures, then the bandeletization procedure pursuits the linear structures adaptively and further removes their correlation. The CBlet transform obtains much sparser representation than the new contourlet transform at the same redundancy. Numerical experiments on image denoising show that the proposed CBlet transform can outperform the new contourlet transform both in term of PSNR and in visual quality.
Detecting and segmenting moving object is an important subject in computer visual analysis. Firstly, the algorithms of detecting moving target from static background in video sequences are discussed in this paper. Secondly, as the inter-frame subtraction can't detect moving object accurately and mixture Gaussian models can't solve the problems such as ghost 、 shadow and real-time application, a new method based on edge-characteristic and inter-frame difference is proposed. In this method the target foreground object can be segregated completely by filling the edge map. Finally, combined with mathematical morphology and connectivity analyzing, noise can be reduced and gaps can be smoothed out. Experimental results show that the proposed algorithm can get exact moving object quickly from complex background and eliminate disturbing of background and the illumination changes efficiently, with operating time reducing dramatically.
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