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Journal of Zhejiang University SCIENCE A 2007 Vol.8 No.8 P.1232-1236

http://doi.org/10.1631/jzus.2007.A1232


Fruit shape detection by level set


Author(s):  GUI Jiang-sheng, RAO Xiu-qin, YING Yi-bin

Affiliation(s):  School of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310029, China

Corresponding email(s):   ybying@zju.edu.cn

Key Words:  Machine vision, Shape detection, Level set, Fruit sorting


GUI Jiang-sheng, RAO Xiu-qin, YING Yi-bin. Fruit shape detection by level set[J]. Journal of Zhejiang University Science A, 2007, 8(8): 1232-1236.

@article{title="Fruit shape detection by level set",
author="GUI Jiang-sheng, RAO Xiu-qin, YING Yi-bin",
journal="Journal of Zhejiang University Science A",
volume="8",
number="8",
pages="1232-1236",
year="2007",
publisher="Zhejiang University Press & Springer",
doi="10.1631/jzus.2007.A1232"
}

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%T Fruit shape detection by level set
%A GUI Jiang-sheng
%A RAO Xiu-qin
%A YING Yi-bin
%J Journal of Zhejiang University SCIENCE A
%V 8
%N 8
%P 1232-1236
%@ 1673-565X
%D 2007
%I Zhejiang University Press & Springer
%DOI 10.1631/jzus.2007.A1232

TY - JOUR
T1 - Fruit shape detection by level set
A1 - GUI Jiang-sheng
A1 - RAO Xiu-qin
A1 - YING Yi-bin
J0 - Journal of Zhejiang University Science A
VL - 8
IS - 8
SP - 1232
EP - 1236
%@ 1673-565X
Y1 - 2007
PB - Zhejiang University Press & Springer
ER -
DOI - 10.1631/jzus.2007.A1232


Abstract: 
A novel approach for fruit shape detection in RGB space was proposed, which was based on fast level set and Chan-Vese model named as Modified Chan-Vese model (MCV). This new algorithm is fast and suitable for fruit sorting because it does not need re-initializing. MCV has three advantages compared to the traditional methods. First, it provides a unified framework for detecting fruit shape boundary, and does not need any preprocessing even though the raw image is noisy or blurred. Second, if the fruit has different colors at the edges, it can detect perfect boundary. Third, it processed directly in color space without any transformations that may lose much information. The proposed method has been applied to fruit shape detection with promising result.

Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article

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