SEGMENTATION OF MUSHROOM AND CAP WIDTH MEASUREMENT USING MODIFIED K-MEANS CLUSTERING ALGORITHM

被引：4
|
作者：
Sert, Eser [1 ]
Okumus, Ibrahim Taner [1 ]
机构：
[1] Kahramanmaras Sutcu Imam Univ, Fac Engn & Architecture, Dept Comp Engn, Avsar Kampusu, TR-46100 Kahramanmaras, Turkey
来源：
ADVANCES IN ELECTRICAL AND ELECTRONIC ENGINEERING | 2014年 / 12卷 / 04期
关键词：
K-Means clustering; mushroom cap measurement; mushroom image segmentation;
D O I：
10.15598/aeee.v12i4.1200
中图分类号：
TM [电工技术]; TN [电子技术、通信技术];
学科分类号：
0808 ; 0809 ;
摘要：
Mushroom is one of the commonly consumed foods. Image processing is one of the effective way for examination of visual features and detecting the size of a mushroom. We developed software for segmentation of a mushroom in a picture and also to measure the cap width of the mushroom. K-Means clustering method is used for the process. K-Means is one of the most successful clustering methods. In our study we customized the algorithm to get the best result and tested the algorithm. In the system, at first mushroom picture is filtered, histograms are balanced and after that segmentation is performed. Results provided that customized algorithm performed better segmentation than classical K-Means algorithm. Tests performed on the designed software showed that segmentation on complex background pictures is performed with high accuracy, and 20 mushrooms caps are measured with 2.281 % average relative error.
引用
收藏
页码：354 / 360
页数：7
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