Human's real life is within a colorful world. Compared to the gray images, color images contain more information and have better visual effects. In today's digital image processing, image segmentation is an important section for computers to "understand" images and edge detection is always one of the most important methods in the field of image segmentation. Edges in color images are considered as local discontinuities both in color and spatial domains. Despite the intensive study based on integration of single-channel edge detection results, and on vector space analysis, edge detection in color images remains as a challenging issue.
A configurable ontology mapping approach based on different kinds of concept feature information is introduced in this paper. In this approach, ontology concept feature information is classified as five kinds, which respectively corresponds to five kinds of concept similarity computation methods. Many existing ontology mapping approaches have adopted the multi-feature reasoning, whereas not all feature information can be computed in the real ontology mapping and only fractional feature information needs to be selected in the mapping computation. Consequently a configurable ontology mapping model is introduced, which is composed of CMT model, SMT model and related transformation model. Through the configurable model, users can conveniently select the most suitable features and configure the suitable weights. Simultaneously, a related 3-step ontology mapping approach is also introduced. Associated with the traditional name and instance learner-based ontology mapping approach, this approach is evaluated by an ontology mapping application example.
分析了C&K度量组中耦合度量指标(CBO)存在的问题,对其进行了改进,提出了一组正交的面向对象耦合度量(Orthog-onal Coupling Metrics Suite for Object-Oriented Design,OCMOOD)。并在Frank Sauer开发的Eclipse度量计算插件的基础上实现了OC-MOOD的自动计算,分析了JUnit3.8.1和JUnit3.7的OCMOOD度量值的计算结果,从而对OCMOOD的有效性进行了验证。
