To solve the problem that when patterns are long, frequent sequential patterns mining may generate an exponential number of results, which often makes decision-makers perplexed for there is too much useless repeated i...
详细信息
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...
详细信息
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...
详细信息
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...
详细信息
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...
详细信息
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...
详细信息
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...
详细信息
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,...
详细信息
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...
详细信息
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...
详细信息
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.
暂无评论