Recognition and localization of occluded apples using K-means clustering algorithm and convex hull theory: a comparison-Reference-Cited by-同舟云学术

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Recognition and localization of occluded apples using K-means clustering algorithm and convex hull theory: a comparison

Published:2015-01-04 Issue:6 Volume:75 Page:3177-3198
ISSN:1380-7501
Container-title:Multimedia Tools and Applications
language:en
Short-container-title:Multimed Tools Appl

Author:

Wang Dandan,Song Huaibo,Tie Zhihui,Zhang Weiyuan,He Dongjian

Publisher

Springer Science and Business Media LLC

Subject

Computer Networks and Communications,Hardware and Architecture,Media Technology,Software

Link

http://link.springer.com/content/pdf/10.1007/s11042-014-2429-9.pdf

Reference27 articles.

1. Bulanon D, Kataoka T, Ota Y, Hiroma T (2002) AE—automation and emerging technologies: a segmentation algorithm for the automatic recognition of Fuji apples at harvest. Biosyst Eng 83(4):405–412

2. Canny J (1986) A computational approach to edge detection. Pattern analysis and machine intelligence. IEEE Trans (6):679–698

3. Fang C, Ma J (2009) A novel k’-means algorithm for clustering analysis. In: biomedical engineering and informatics. IEEE 1–5

4. Feng J, Wang S, Liu G, Zeng L (2012) A separating method of adjacent apples based on machine vision and chain code information. In: Computer and Computing Technologies in Agriculture V. Springer. 258–267

5. Gonzalez R, Woods R, Eddins S (2009) Digital image processing using MATLAB, vol 2. Gatesmark Publishing, Knoxville

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