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Transactions: WSEAS TRANSACTIONS ON COMPUTERS
Transactions ID Number: 31-817
Full Name: Changyong Li
Position: Ph.D. Candidate
Age: ON
Sex: Male
Address: Research Institute of Robotics,Shanghai Jiao Tong University,Shanghai
Country: CHINA
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E-mail address: LLCYong@126.com
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Title of the Paper: a method for color classification of fruits based on machine vision
Authors as they appear in the Paper: Changyong Li , Qixin Cao, Feng Guo
Email addresses of all the authors: LLCYong@126.com
Number of paper pages: 10
Abstract: A dominant color histogram matching method for fruits classification was presented in this paper. In classification of fruits based on machine vision, image was acquired with a color CCD camera that outputted color information in three channels, red, green, and blue. Because traditional RGB color space couldn¡¯t meet subjective color sensation of human being, so color image needed to be transformed from RGB to HSV color space which represented human being's subjective color knowledge. However, the conversion result was still three-dimensional information that made determining color grades very difficult. A new color space conversion technique that could be implemented for high-speed real-time processing for color grading was introduced in this paper. The result of this technique was a simple one-dimensional array that represented different color levels .These colors were known as dominant colors of fruits. The technique reduced computation consumption greatly. Colo!
r histogram as a statistical feature had visual invariance and high robustness. The dominant color histogram matching method was used for color grading. Grade judgment result was given by calculating and comparing the similarity between the inspected sample histogram and standard template histogram for every grade, fruit sample would be assigned to the grade whose template had the biggest similarity with it. Experiment results show that dominant color histogram matching method has high accuracy in fruits¡¯ color classification.
Keywords: Fruit classification, Color grading, Color classification, Histogram matching, Dominant color, Machine vision, Feature extraction
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