Segmentation of Mushroom and Cap width Measurement using Modified K-Means Clustering Algorithm

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 clusterin...

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Bibliographic Details
Published in:Advances in electrical and electronic engineering Vol. 12; no. 4; pp. 354 - 360
Main Authors: Sert, Eser, Okumus, Ibrahim Taner
Format: Journal Article
Language:English
Published: Ostrava Faculty of Electrical Engineering and Computer Science VSB - Technical University of Ostrava 01.01.2014
VSB-Technical University of Ostrava
Subjects:
Algorithms
Cluster analysis
Computer programs
Customizing
k-means clustering
mushroom cap measurement
mushroom image segmentation
Mushrooms
Pictures
Segmentation
Software
ISSN:1336-1376, 1804-3119
Online Access:Get full text
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Summary: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 % relative error.
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ISSN:1336-1376
1804-3119
DOI:10.15598/aeee.v12i4.1200