A new method is presented for robustly estimating fundamental matrix from matched points. The method comprises two parts. The first uses a robust technique-the random sample consensus (RANSAC) to discard outliers in a...
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This paper is concerned with solution of the consistent fundamental matrix estimation in a quadratic measurement error model. First an extended system for determining the estimator is proposed, and an efficient implem...
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Under the framework of LPU (learning from positive data and unlabeled data), this paper originally proposes a three-step algorithm. First, Co-Training is employed for filtering out the "suspect positive" dat...
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The proxy cache for streaming media is the important method to economize the resources of the Internet. The cache policies influence the effect for proxy cache. In this paper, based on the client's request rate, c...
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In this paper, a relatively flexible filter called extended bilateral filter is proposed, by which some particular filters can be designed via selecting an appropriate pixel of interest (POI) and defining a kernel for...
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Fourier-Mellin transform (FMT) is frequently used in content-based image retrieval and digital image watermarking. This paper extends the application of FMT into image registration and proposes an improved registratio...
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In this article, we investigate the problem of preparing qualitative spatial relations before implementing spatial data mining by checking consistency in a constraint network, which includes topological and cardinal d...
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Intensity degradations are a familiar problem for fluorescein angiogram sequences. In this paper, we attempt to super-resolve a fluorescein angiogram, and to keep the high intensity pixels from degrading. To this end,...
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A voting-mechanism-based fuzzy neural network system is proposed in this paper. When constructing the network structure, a generalized class cover problem is presented and its two solving algorithm, an improved greedy...
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In this paper, we propose a new supervised compound learning algorithm for training our constructed approximated bivariate non-tensor product adaptive pre-wavelet neural network (APWNN). On the one hand, the linear we...
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In this paper, we propose a new supervised compound learning algorithm for training our constructed approximated bivariate non-tensor product adaptive pre-wavelet neural network (APWNN). On the one hand, the linear weights of APWNN are trained by the self-adaptive learning rate method. On the other hand an extended Kalman filter method is used to update the nonlinear parameters such as dilation parameters and translation parameters. Additionally we demonstrate the efficiency of our proposed method through a concrete example of function approximation.
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