Co-Occurrence Matrix-Based Statistical Model for Texture Analysis from Images

摘要：

Texture surface analysis is very important for machine vision system. We explore Gray Level Cooccurrence Matrix-based 2nd order statistical features to understand image texture surface. We employed several features on our ground-truth dataset to understand its nature; and later employed it in a building dataset. Based on our experimental results, we can conclude that these image features can be useful for texture analysis and related fields.

关键词： Texture analysis co-occurrence matrix statistical model GLCM image feature entropy

作者: SHAHERA Hossain SEIICHI Serikawa

作者单位: Department of Electrical Engineering, Kyushu Institute of Technology 1-1 Sensui-ho, Tobata-ku, Kitak Department of Electrical Engineering, Kyushu Institute of Technology 1-1 Sensui-cho, Tobata-ku, Kita

会议类型: 国际会议

会议名称: The 3th International Conference on Precision Instrumentation and Measurement 2011(CPIM2011)(第三届精密仪器与测量国际学术会议)

会议地点: 湘潭

会议语种:英文

页码: 717-724

在线出版日期: 2011-07-19（万方平台首次上网日期，不代表论文的发表时间）