DocumentCode :
547792
Title :
Apple defect detection using statistical histogram based EM algorithm
Author :
Moradi, Ghobad ; Shamsi, Mousa ; Sedaaghi, Mohammad Hossein ; Moradi, Setareh ; Alsharif, Mohammad Reza
Author_Institution :
Young Researcher Club, Branch of Kermanshah Azad Univ., Kermanshah, Iran
fYear :
2011
fDate :
17-19 May 2011
Firstpage :
1
Lastpage :
6
Abstract :
Segmentation of an image into its components plays an important role in most of the image processing applications. In this article an important application of image processing in determination of apple quality is studied, and an automatic algorithm is proposed in order to determine apples skin color defects. First, this image is converted from RGB to color space L* a* b*. Then fruit shape is extracted by A CM algorithm. Finally, the image has segmented using SHEM a lgor ithm. Ex per im ental r esults on the a cquir ed im ages show that both EM and SHEM spend the same iterations to accomplish the segmentation process and get the same results. However, the proposed SHEM algorithm consumes less time than the standard EM algorithm. Accuracy of the proposed algorithm on the acquired images is 91.72% and 94.86% for healthy pixels and defected ones, respectively.
Keywords :
image colour analysis; image segmentation; iterative methods; statistical analysis; ACM algorithm; EM algorithm; RGB; SHEM algorithm; apple defect detection; apples skin color defects; automatic algorithm; healthy pixels; image processing application; image segmentation; iteration; statistical histogram; Accuracy; Classification algorithms; Histograms; Image color analysis; Image segmentation; Pixel; Image segmentation; active counter model; apple defects; color space; expectation maximization algorithm; statistical histogram;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Electrical Engineering (ICEE), 2011 19th Iranian Conference on
Conference_Location :
Tehran
Print_ISBN :
978-1-4577-0730-8
Electronic_ISBN :
978-964-463-428-4
Type :
conf
Filename :
5955681
Link To Document :
بازگشت