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Azizi Abdullah
Faculty of Information Science and Technology, Universiti Kebangsaan Malaysia, Bangi, 43600 UKM, Selangor D.E.
Computer Science & IT

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Hybrid Neural Network and Linear Model for Natural Produce Recognition Using Computer Vision Joko Siswantoro; Anton Satria Prabuwono; Azizi Abdullah; Bahari Indrus
Journal of ICT Research and Applications Vol. 11 No. 2 (2017)
Publisher : LPPM ITB

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.5614/itbj.ict.res.appl.2017.11.2.5

Abstract

Natural produce recognition is a classification problem with various applications in the food industry. This paper proposes a natural produce recognition method using computer vision. The proposed method uses simple features consisting of statistical color features and the derivative of radius function. A hybrid neural network and linear model based on a Kalman filter (NN-LMKF) was employed as classifier. One thousand images from ten categories of natural produce were used to validate the proposed method by using 5-fold cross validation. The experimental result showed that the proposed method achieved classification accuracy of 98.40%. This means it performed better than the original neural network and k-nearest neighborhood.
Co-Authors Anton Satria Prabuwono Bahari Indrus Joko Siswantoro