We propose a novel approach for solving the perceptual grouping problem in vision. Rather than fo-cusing on local features and their consistencies in the image data, our approach aims at extracting the global impression of an image. We treat image segmenta-tion as a graph partitioning problem and propose a novel global criterion, the normalized cut, for segmenting the graph. The normalized cut ...

This paper proposes a practical stability analysis of a particular class of continuous PI-like fuzzy control systems based on the convergence of regular vector norms. This approach is based on the comparison, the overvaluing principle and the application of Borne and Gentina criterion. The stability conditions issued from vector norms correspond to a vector Lyapunov function. A comparison syste...

‘‘Case-1’’ has been a term frequently used to characterize water type since the seventies. However, the distribution of Case-1 waters in global scale has been vague, though open ocean waters are often referred to as Case-1 in the literature. In this study, based on recent bio-optical models for Case-1 waters, an inclusive and quantitative Case-1 criterion for remote sensing applications is deve...

This paper addresses the problem of learning Bayesian network structures from data based on score functions that are decomposable. It describes properties that strongly reduce the time and memory costs of many known methods without losing global optimality guarantees. These properties are derived for different score criteria such as Minimum Description Length (or Bayesian Information Criterion)...

This paper develops a framework for optimizing global-local hybrids of search or optimization procedures. The paper starts by idealizing the search problem as a search by a global algorithm G for either (1) acceptable targets|solutions that meet a speci ed criterion|or for (2) basins of attraction that then lead to acceptable targets under a speci ed local search algorithm L. The paper continue...

Kernel-based feature extraction is widely used in image classification, and different kernel methods extract the features based different criterion. KPCA maximizes the determinant of the total scatter matrix of the transformed sample, while KDA seeks the direction of discrimination. KPCA preserves the global property, and KDA utilizes class information to enhance its discriminative ability so a...

In this paper, a new approach on image registration is presented. We introduce a novel conceptionnormal vector information (NVI) to evaluate the similarity between two images. NVI method takes advantage of the relationship between voxels in the image to extract the normal vector (NV) information of each voxel. Firstly, NVI criterion is presented. Then, based on the criterion, we find that NVI r...