Abstract : In this paper, a novel method to recognize defect regions of apples based on Gabor wavelet transformation and SVM using machine vision is proposed. The method starts with background removal and object segmentation by threshold. Texture features are extracted from each segmented object by using Gabor wavelet transform, and these features are introduced to support vector machines (SVM) classifiers. Experimental results exhibit correctly recognized 85% of the defect regions of apples.
Daoliang Li; Yingyi Chen. 5th Computer and Computing Technologies in Agriculture (CCTA), Oct 2011, Beijing, China. Springer, IFIP Advances in Information and Communication Technology, AICT-370 (Part III), pp.343-350, 2012, Computer and Computing Technologies in Agriculture V. 〈10.1007/978-3-642-27275-2_39〉
https://hal.inria.fr/hal-01361158
Contributeur : Hal Ifip
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Dernière modification le : mardi 6 septembre 2016 - 17:40:16
Document(s) archivé(s) le : mercredi 7 décembre 2016 - 14:22:22
Wenqian Huang, Chi Zhang, Baihai Zhang. Identifying Apple Surface Defects Based on Gabor Features and SVM Using Machine Vision. Daoliang Li; Yingyi Chen. 5th Computer and Computing Technologies in Agriculture (CCTA), Oct 2011, Beijing, China. Springer, IFIP Advances in Information and Communication Technology, AICT-370 (Part III), pp.343-350, 2012, Computer and Computing Technologies in Agriculture V. 〈10.1007/978-3-642-27275-2_39〉. 〈hal-01361158〉
