19740000
Color Image Segmentation Based on Parameter-Dependent Connected Components
A theory of a parameter-dependent connected components of gray images that takes into account both the gray values of the pixels and the differences of the gray values of the neighboring pixels has been introduced in a previous work. The concept of the parameter-dependent connected components has been applied to gray-scaled images for segmentation successfully. In this paper, the author investigates a method to apply the concept of the parameterdependent connected components to color images for segmentation.
43870484
On parameter-dependent connected components of gray images
Abstract We introduce a new definition of the connected components of gray images which takes into account both the gray values of the pixels and the differences of the gray values of the neighbouring pixels and which depends on two parameters, e and δ. We may adjust the values of the parameters for different types of images or in different levels of image analysis. We analyse a gray image in an intermediate level through the inter-relationships of its (e, δ) components rather than only in the pixel level.
22289409
Attribute Clustering for Grouping, Selection, and Classification of Gene Expression Data
18961070
A probabilistic approach for foreground and shadow segmentation in monocular image sequences
This paper presents a novel method of foreground and shadow segmentation in monocular indoor image sequences. The models of background, edge information, and shadow are set up and adaptively updated. A Bayesian network is proposed to describe the relationships among the segmentation label, background, intensity, and edge information. A maximum a posteriori-Markov random field estimation is used to boost the spatial connectivity of segmented regions.
7846558
T1DBase, a community web-based resource for type 1 diabetes research
T1DBase (http://T1DBase.org) is a public website and database that supports the type 1 diabetes (T1D) research community. The site is currently focused on the molecular genetics and biology of T1D susceptibility and pathogenesis. It includes the following datasets: annotated genome sequence for human, rat and mouse; information on genetically identified T1D susceptibility regions in human, rat and mouse, and genetic linkage and association studies pertaining to T1D; descriptions of NOD mouse congenic strains; the Beta Cell Gene Expression Bank, which reports expression levels of genes in beta cells under various conditions, and annotations of gene function in beta cells; data on gene expression in a variety of tissues and organs; and biological pathways from KEGG and BioCarta. Tools on the site include the GBrowse genome browser, site-wide context dependent search, Connect-the-Dots for connecting gene and other identifiers from multiple data sources, Cytoscape for visualizing and analyzing biological networks, and the GESTALT workbench for genome annotation. All data are open access and all software is open source.
18765129
Security Analysis of Enterprise Network Based on Stochastic Game Nets Model
In this paper, we propose a novel modeling method, Stochastic Game Nets (SGN), and use it to model and analyze the security issues in enterprise networks. Firstly, the definition and modeling algorithm of Stochastic Game Nets are given. And then we apply the Stochastic Game Nets method to describe the attack and defense course in the enterprise networks successfully, and find a Nash equilibrium. Finally we analyze the confidentiality and integrity of the enterprise network quantificationally based on the model. The method can also be applied to other areas with respect to a game.
16300996
Research on Optimization Algorithm for Attribute Reduction of Decision System
Attribute reduction is one of the central issue in the theoretical research of rough set, aiming at the NP-hard problem of acquiring minimal reduction and all reduction, an optimization algorithm is proposed to construct reduction tree based on discernibility matrix for acquiring attribute reduction. The discernibility set is acquired by improving discernibility matrix first, then the core and reduction candidate information are obtained, finally the minimal reduction and all reduction are sought fast and effectively by considering attribute frequency as heuristic information, and time cost of the algorithm is analyzed. The time complexity of the algorithm is lower than other presented algorithms.