DocumentCode :
2147957
Title :
Sewage Image Feature Extraction and Turbidity Degree Detection Based on Embedded System
Author :
Gao, Meijuan ; Tian, Jingwen ; Ai, Lan ; Zhang, Fan
Author_Institution :
Dept. of Autom. Control, Beijing Union Univ., Beijing
fYear :
2008
fDate :
30-31 Dec. 2008
Firstpage :
357
Lastpage :
360
Abstract :
Sewage turbidity degree is an important judgment standard of primary processed wastewater. Embedded system (ARM) platform has excellences of low power consumption, dexterity, good community ability. A detection method of sewage turbidity degree based on ARM is proposed. The image sensors and light intensity are used to combine with ARM system and obtain the sewage image, and a kind of image gather and analyzing system is composed. The edge detection algorithm and image enhancement of image processing are used to treat the obtained image. We extract sewage turbidity degree characters and judge whether the sewage image is qualified, and realize image detection algorithm on ARM system. ARM system judges sewage qualified or not based on result of image detection arithmetic and information of light intensity sensor. The method is validated by simulation on ARM platform.
Keywords :
edge detection; embedded systems; feature extraction; image enhancement; optical sensors; sewage treatment; turbidity; ARM system; edge detection algorithm; embedded system; image detection algorithm; image enhancement; image feature extraction; image processing; image sensors; light intensity sensor; sewage image; sewage treatment; sewage turbidity degree detection; wastewater; Data mining; Embedded system; Energy consumption; Feature extraction; Image analysis; Image edge detection; Image enhancement; Image processing; Image sensors; Wastewater; detection; embedded system; image feature extraction; sewage treatment; sewage turbidity degree;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
MultiMedia and Information Technology, 2008. MMIT '08. International Conference on
Conference_Location :
Three Gorges
Print_ISBN :
978-0-7695-3556-2
Type :
conf
DOI :
10.1109/MMIT.2008.196
Filename :
5089133
Link To Document :
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