Share Email Print

Proceedings Paper

An automatic segmentation method for multi-tomatoes image under complicated natural background
Author(s): Jianjun Yin; Hanping Mao; Yongguang Hu; Xinzhong Wang; Shuren Chen
Format Member Price Non-Member Price
PDF $17.00 $21.00
cover GOOD NEWS! Your organization subscribes to the SPIE Digital Library. You may be able to download this paper for free. Check Access

Paper Abstract

It is a fundamental work to realize intelligent fruit-picking that mature fruits are distinguished from complicated backgrounds and determined their three-dimensional location. Various methods for fruit identification can be found from the literatures. However, surprisingly little attention has been paid to image segmentation of multi-fruits which growth states are separated, connected, overlapped and partially covered by branches and leaves of plant under the natural illumination condition. In this paper we present an automatic segmentation method that comprises of three main steps. Firstly, Red and Green component image are extracted from RGB color image, and Green component subtracted from Red component gives RG of chromatic aberration gray-level image. Gray-level value between objects and background has obviously difference in RG image. By the feature, Ostu's threshold method is applied to do adaptive RG image segmentation. And then, marker-controlled watershed segmentation based on morphological grayscale reconstruction is applied into Red component image to search boundary of connected or overlapped tomatoes. Finally, intersection operation is done by operation results of above two steps to get binary image of final segmentation. The tests show that the automatic segmentation method has satisfactory effect upon multi-tomatoes image of various growth states under the natural illumination condition. Meanwhile, it has very robust for different maturity of multi-tomatoes image.

Paper Details

Date Published: 7 December 2006
PDF: 8 pages
Proc. SPIE 6411, Agriculture and Hydrology Applications of Remote Sensing, 641118 (7 December 2006); doi: 10.1117/12.697799
Show Author Affiliations
Jianjun Yin, Jiangsu Univ. (China)
Hanping Mao, Jiangsu Univ. (China)
Yongguang Hu, Jiangsu Univ. (China)
Xinzhong Wang, Jiangsu Univ. (China)
Shuren Chen, Jiangsu Univ. (China)

Published in SPIE Proceedings Vol. 6411:
Agriculture and Hydrology Applications of Remote Sensing
Robert J. Kuligowski; Jai S. Parihar; Genya Saito, Editor(s)

© SPIE. Terms of Use
Back to Top