Algorithm for Size Statistic and Clustering of Block Image Sequences
DOI: 10.23977/acss.2016.11004 | Downloads: 57 | Views: 6313
Author(s)
Zou Shaoli 1, Li Cong 2
Affiliation(s)
1 No 5 Shanda Road, A judge in Shandong Training College, Jinan, China
2 No 106 Jiweit Road, School of Electrical Engineering, University of Jinan, Jinan, China
Corresponding Author
Li CongABSTRACT
The statistic algorithm for image processing, block clustering and block size is proposed according to the block image sequences. For complicated and regular distribution block image. Firstly, we get the binary picture, and calculate the ratio of the edge and ground and the maximum distance between the two edges by the pixel scanning method, then we can get the data sample of the size and block size distribution at the same time. Secondly, using the improved nearest neighbor algorithm[5], we get the block size classification, and the percentage of each regional size in the whole area. Computer emulate result proved that this method meets the demands of the real-time image processing better for its advantage as follows: the small calculation, the high precision. The algorithm will lay a solid foundation for the operating mode analysis and automatic control.
KEYWORDS
Shot blasting machine; Programmable logic controller; Ethernet; Kingview; MonitoringCITE THIS PAPER
Cong, L. and Shaolin, Z. (2016) Algorithm for Size Statistic and Clustering of Block Image Sequences. Advances in Computer, Signals and Systems (2016) 1: 18-22.
REFERENCES
[1] CAO Peng-Fei, LUO Xiong-Lin. Wiener Structure Based Modeling and Identifying of Soft Sensor Systems[J]. Acta Automatica Sinica. 2014,40(10):2179-2192.
[2] Wu Xiao-xun. A Study on Burning State Recognitionof Clinker Based on Burning Zone Image[D]. Shen yang: North eastern University June 2014.
[3] LIU Jin-Xin, ZHU Yun-Long, SHEN Zhe, et al. Hybrid Recognition Method for Burning Zone Condition of Rotary Kiln[J]. Acta Automatica Sinica. 2012,38(7):1153-1161.
[4] SHI Pei-yuan, DENG Tingquan. Fuzzy-based color recognition and its application in image retrieval. Computer Engineering and Applications[J], 2013, 49(18):138-141.
[5] Li Cong, Zhang Yong, Gao Zhi. A new clustering algorithm [J]. Pattem Recognition and Aitificial Intelligence. 1999,12(2):205-208.
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