{"title":"基于非对称和反填充模型的彩色图像几何矩精确计算","authors":"Yunping Zheng, Yi-Hsin Chang, M. Sarem","doi":"10.17706/IJCCE.2017.6.1.19-28","DOIUrl":null,"url":null,"abstract":"Accurate computation of geometric moments is very important in computer vision, image processing and pattern recognition. In this paper, inspired by the idea of bit-plane decomposition and accurate computation of geometric moments on binary images, we put forward an accurate and fast algorithm for the computation of geometric moments using Non-symmetry and Anti-packing Model (NAM) for color images, which takes O(N) time where N is the number of all NAM blocks. By taking four color images ‘Lena’, ‘Peppers’, ‘Frog’, and ‘Fish’ as typical test objects, and by comparing our proposed NAM-based accurate algorithm with the popular Binary Tree (BT)-based accurate algorithm for computing the geometric moments, the theoretical and experimental results presented in this paper show that our NAM-based accurate algorithm can significantly improve the execution speed by 43.71%, 41.93%, 41.01%, and 38.63% over the BT-based accurate algorithm in images ‘Lena’, ‘Peppers’, ‘Frog’, and ‘Fish’, respectively. Also, our NAM-based accurate algorithm can significantly improve the average execution speed by 41.32% over the BT-based accurate algorithm. Therefore, in the case of computing lower order moments of color images, our proposed accurate algorithm is much faster than the BT-based accurate algorithm.","PeriodicalId":23787,"journal":{"name":"World Academy of Science, Engineering and Technology, International Journal of Electrical, Computer, Energetic, Electronic and Communication Engineering","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2017-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"Accurate Computation of Geometric Moments Using Non-symmetry and Anti-packing Model for Color Images\",\"authors\":\"Yunping Zheng, Yi-Hsin Chang, M. Sarem\",\"doi\":\"10.17706/IJCCE.2017.6.1.19-28\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Accurate computation of geometric moments is very important in computer vision, image processing and pattern recognition. In this paper, inspired by the idea of bit-plane decomposition and accurate computation of geometric moments on binary images, we put forward an accurate and fast algorithm for the computation of geometric moments using Non-symmetry and Anti-packing Model (NAM) for color images, which takes O(N) time where N is the number of all NAM blocks. By taking four color images ‘Lena’, ‘Peppers’, ‘Frog’, and ‘Fish’ as typical test objects, and by comparing our proposed NAM-based accurate algorithm with the popular Binary Tree (BT)-based accurate algorithm for computing the geometric moments, the theoretical and experimental results presented in this paper show that our NAM-based accurate algorithm can significantly improve the execution speed by 43.71%, 41.93%, 41.01%, and 38.63% over the BT-based accurate algorithm in images ‘Lena’, ‘Peppers’, ‘Frog’, and ‘Fish’, respectively. Also, our NAM-based accurate algorithm can significantly improve the average execution speed by 41.32% over the BT-based accurate algorithm. Therefore, in the case of computing lower order moments of color images, our proposed accurate algorithm is much faster than the BT-based accurate algorithm.\",\"PeriodicalId\":23787,\"journal\":{\"name\":\"World Academy of Science, Engineering and Technology, International Journal of Electrical, Computer, Energetic, Electronic and Communication Engineering\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2017-01-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"World Academy of Science, Engineering and Technology, International Journal of Electrical, Computer, Energetic, Electronic and Communication Engineering\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.17706/IJCCE.2017.6.1.19-28\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"World Academy of Science, Engineering and Technology, International Journal of Electrical, Computer, Energetic, Electronic and Communication Engineering","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.17706/IJCCE.2017.6.1.19-28","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Accurate Computation of Geometric Moments Using Non-symmetry and Anti-packing Model for Color Images
Accurate computation of geometric moments is very important in computer vision, image processing and pattern recognition. In this paper, inspired by the idea of bit-plane decomposition and accurate computation of geometric moments on binary images, we put forward an accurate and fast algorithm for the computation of geometric moments using Non-symmetry and Anti-packing Model (NAM) for color images, which takes O(N) time where N is the number of all NAM blocks. By taking four color images ‘Lena’, ‘Peppers’, ‘Frog’, and ‘Fish’ as typical test objects, and by comparing our proposed NAM-based accurate algorithm with the popular Binary Tree (BT)-based accurate algorithm for computing the geometric moments, the theoretical and experimental results presented in this paper show that our NAM-based accurate algorithm can significantly improve the execution speed by 43.71%, 41.93%, 41.01%, and 38.63% over the BT-based accurate algorithm in images ‘Lena’, ‘Peppers’, ‘Frog’, and ‘Fish’, respectively. Also, our NAM-based accurate algorithm can significantly improve the average execution speed by 41.32% over the BT-based accurate algorithm. Therefore, in the case of computing lower order moments of color images, our proposed accurate algorithm is much faster than the BT-based accurate algorithm.