ROBUST POTATO COLOR IMAGE SEGMENTATION USING ADAPTIVE FUZZY INFERENCE SYSTEM

Potato image segmentation is an important part of image-based potato defect detection. This paper presents a robust potato color image segmentation through a combination of a fuzzy rule based system, an image thresholding based on Genetic Algorithm (GA) optimization and morphologi- cal operators. The proposed potato color image segmentation is robust against variation of background, distance and view of potato from digital camera. In the proposed algorithm, after selecting appropriate color space, distance be- tween an image pixel and real potato pixels is computed. Furthermore, this distance feeds to a fuzzy rule-based classier to extract potato candidate in the input image. A subtractive clustering algorithm is also used to decide on the number of rules and membership functions of the fuzzy system. To improve the performance of the fuzzy rule-based classier, the membership functions shapes are also optimized by the GA. To segment potatoes in the input color image, an image thresholding is applied to the output of the fuzzy system, where the corresponding threshold is optimized by the GA. To im- prove the segmentation results, a sequence of some morphological operators are also applied to the output of thresholding stage. The proposed algorithm is applied to dierent databases with dierent backgrounds, including USDA, CFIA, and obtained potato images database from Ardabil (Iran's northwest), separately. The correct segmentation rate of the proposed algorithm is approx- imately 98% over totally more than 500 potato images. Finally, the results of the proposed segmentation algorithm are evaluated for some images taken from real environments of potato industries and farms.

[1]  Navid Razmjooy,et al.  A multi layer perceptron neural network trained by Invasive Weed Optimization for potato color image segmentation. , 2012 .

[2]  A Vyawahare,et al.  Computer Vision System for Colour Measurement - Fundamentals and Applications in Food Industry: A Review , 2013 .

[3]  S. Amirhassan Monadjemi,et al.  A novel fuzzy rule base system for pose independent faces detection , 2011, Appl. Soft Comput..

[4]  Y.E. Fernandez,et al.  Development of a prototype for classification of potato mini-tubers based on artificial vision , 2009, 2009 6th International Conference on Electrical Engineering, Computing Science and Automatic Control (CCE).

[5]  Rafael C. González,et al.  Digital image processing using MATLAB , 2006 .

[6]  L. Yang.  Fuzzy Logic with Engineering Applications , 1999 .

[7]  Li Wei,et al.  Automatic detecting and grading method of potatoes with computer vision. , 2009 .

[8]  B. Ripley,et al.  Pattern Recognition , 1968, Nature.

[9]  Chieh-Li Chen,et al.  Adaptive fuzzy color segmentation with neural network for road detections , 2010, Eng. Appl. Artif. Intell..

[10]  Yongmin Kim,et al.  PC-based machine vision system for real-time computer-aided potato inspection , 1998, Int. J. Imaging Syst. Technol..

[11]  Da-Wen Sun,et al.  Computer vision technology for food quality evaluation , 2008 .

[12]  Domingo Mery,et al.  Development of a computer vision system to measure the color of potato chips , 2006 .

[13]  Charles T. Morrow,et al.  An automated inspection station for machine-vision grading of potatoes , 2005, Machine Vision and Applications.

[14]  Chih-Chin Lai,et al.  A Novel Image Segmentation Approach Based on Particle Swarm Optimization , 2006, IEICE Trans. Fundam. Electron. Commun. Comput. Sci..

[15]  Miss A.O. Penney.  (b) , 1974, The New Yale Book of Quotations.

[16]  Andreas Koschan,et al.  Digital Color Image Processing , 2008 .

[17]  H. Gonzalez-Jorge,et al.  A genetic algorithm approach for feature selection in potatoes classification by computer vision , 2009, 2009 35th Annual Conference of IEEE Industrial Electronics.

[18]  Re Gonzalez,et al.  R.C. Eddins, Digital image processing using MATLAB, vol. Gatesmark Publishing Knoxville , 2009 .

[19]  Bülent Sankur,et al.  Survey over image thresholding techniques and quantitative performance evaluation , 2004, J. Electronic Imaging.

[20]  Sergios Theodoridis,et al.  Pattern Recognition, Fourth Edition , 2008 .

[21]  Takashi Kataoka,et al.  An image processing algorithm for detecting in-line potato tubers without singulation , 2010 .

[22]  Kaveh Mollazade,et al.  Detection of Greening in Potatoes using Image Processing Techniques , 2011 .

[23]  P. Kanungo,et al.  Image Segmentation Using Thresholding and Genetic Algorithm , 2006 .

[24]  Wang Chenglong,et al.  Detection of potato's size based on centroidal principal axis , 2011 .

[25]  Shuoshi Ma,et al.  Studyed On The Externaldefects Segmentation Based On The Color Character Of Potatoes , 2008, CCTA.

[26]  C. T. Morrow,et al.  Machine Vision for Color Inspection of Potatoes and Apples , 1995 .

[27]  R. A. Rahmat,et al.  GENERATION OF FUZZY RULES WITH SUBTRACTIVE CLUSTERING , 2005 .

[28]  Grzegorz Cielniak,et al.  Visual detection of blemishes in potatoes using minimalist boosted classifiers , 2010 .

[29]  Jing Jin,et al.  Methodology for Potatoes Defects Detection with Computer Vision , 2009 .

[30]  J. C. Noordam,et al.  A colour vision system for high speed sorting of potatoes , 2000 .

[31]  Navid Razmjooy,et al.  Computer Vision-Based Potato Defect Detection using Neural Networks and Support Vector Machine , 2013, Int. J. Robotics Autom..

[32]  J. C. Noordam,et al.  Multivariate image segmentation with cluster size insensitive fuzzy C-means , 2002 .