DocumentCode :
545433
Title :
Key of packaged granary grain quantity recognition —Grain bags image processing
Author :
Liu, Yong
Author_Institution :
Sch. of Manage., Chongqing Jiaotong Univ., Chongqing, China
Volume :
2
fYear :
2011
fDate :
11-13 March 2011
Firstpage :
292
Lastpage :
296
Abstract :
A study of core problem in the packaged granary grain intelligent detection based on image recognition was conducted, and an intelligent detection method combining Fisher criterion with Adaptive Genetic Algorithm was used to solve it. We took the actual scene video as the analysis object, and the constructed Fisher criterion as the fitness function of Genetic Algorithm, while we presented a local optimization operator that enhance the diversity of the population and solve the disadvantages of poor astringency and premature occurrence. Finally, we introduced the morphology processing method to achieve excellent detection effect. Experimental results showed that this detection algorithm effectively improves the anti-jamming capability and robustness. This work provides an intelligent detection method for grain reserving management.
Keywords :
agricultural products; bags; genetic algorithms; image recognition; mathematical morphology; object detection; video signal processing; Fisher criterion; adaptive genetic algorithm; antijamming capability; fitness function; grain bags image processing; image recognition; morphology processing method; object detection; packaged granary grain intelligent detection; packaged granary grain quantity recognition; video scene; Algorithm design and analysis; Feature extraction; Genetic algorithms; Image edge detection; Image segmentation; Optimization; Fisher criterion; adaptive genetic algorithm; grain bags image processing; local optimization operator;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Computer Research and Development (ICCRD), 2011 3rd International Conference on
Conference_Location :
Shanghai
Print_ISBN :
978-1-61284-839-6
Type :
conf
DOI :
10.1109/ICCRD.2011.5764135
Filename :
5764135
Link To Document :
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