In order to create a seamless and seemingly natural panorama, we propose a novel stitching method when a panoramic scene contain two predominate planes. Firstly, compute each homography of per planes. Then, how to set the each of weight in dual-homography become an important step. The traditional method of setting weights is to directly calculate European distance between original image location pixel points and feature points. The disadvantage is the weights of singular points seriously impact the overall decision. In this paper, we proposed a static probability model of error matching to optimize weights by multivariate student’s t distribution. No only error matching probability, but also error amount and distance of feature points are all considered in the weight model. Finally, a renewal single homography is defined by establishing contact between dual-homography and weights. Experiments show the homography matrix is more robust and accurate to perform a nonlinear warping. The proposed method is easily generalized to multiple images, and allows one to automatically obtain the best perspective in the panorama.
Since a lot of speckles in SAR images, there are a lot of uncertainty in SAR image. It brings a lot of difficulty to the targets detection. Fuzzy theory is a mathematical method used to reduce this uncertainty. A new FSII-CFAR detector is proposed, which is improved intelligent iterative CFAR detection by searching a better fitting distribution model of SAR image background based on fuzzy logic. The best fitting distribution model of background data is decided by the membership value of fuzzy clustering criterion (FCC). Compared with traditional fitting criterion, the results of the FCC improve the detection rate of CFAR. Because the fitting results are more approximated to SAR image background, the simulation results show that the FSII-CFAR detector can make the detection rate reach more than 80% in complex background.