NDLI: Color and texture for corn seed classification by machine vision

Content Provider	IEEE Xplore Digital Library
Author	Kiratiratanapruk, K. Sinthupinyo, W.
Copyright Year	2011
Description	Author affiliation: National Electronics and Computer Technology Center, National Science and Technology Development Agency, Pathumthani, Thailand (Kiratiratanapruk, K.; Sinthupinyo, W.)
Abstract	Machine vision has been applied to various food materials inspection process of agricultural industry in order to achieve fast and accurate operation. In this paper, we proposed a method to classify more than ten categories of seed defects by using color, texture features and support vector machine (SVM) type classifier. We also developed an image capturing machine that is able to support large volume of seed samples. The image capturing machine was designed to control uncertainty light level, reflection and shadow appeared on seed samples. Therefore, quality of images that can be obtained from the machine in term of accurate color and exposure is high. In addition, the designed image capturing machine also provides support to background subtraction and touching object segmentation processes. In the image classification part, color histograms in RGB and HSV color space together with texture based on Grey level co-occurrence matrix (GLCM) and Local binary pattern (LBP) is adopted as features. The proposed systems were evaluated from more than 10,000 sample images. The obtained accuracies are 95.6% for normal seed type and 80.6% for group of defect seed types. The preliminary results of this study are useful information for future development of the quality control technique in practical usage.
Starting Page	1
Ending Page	5
File Size	1099826
Page Count	5
File Format	PDF
ISBN	9781457721656
e-ISBN	9781457721663
DOI	10.1109/ISPACS.2011.6146100
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-12-07
Publisher Place	Thailand
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Physics SVM Corn Seed Grain Color Texture Image Processing Classification
Content Type	Text
Resource Type	Article

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