A Code Based Fruit Recognition Method Via Image Convertion Using Multiple Features

This research is to propose a fast and accurate object recognition method especially for fruit recognition to be used for mobile environment. Conventional techniques are based on one or more of basic features that characterize an object: color, shape, texture and intensity, causing performance limitation for mobile environment. Thus, this paper presents a combined approach that transforms those basic features into their associated code fields to generate an object code that could be used as a search key for the feature database. Experimental results have been collected using a fruit database consisting of 33 different classes of fruits and 1006 fruits overall. Thus, average accuracy of more than 90% is obtained and performance increases compared to other approaches on fruit image recognition.

[1]  Seyed Hadi Mirisaee,et al.  A new method for fruits recognition system , 2009, 2009 International Conference on Electrical Engineering and Informatics.

[2]  Tom Drummond,et al.  Faster and Better: A Machine Learning Approach to Corner Detection , 2008, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[3]  Jacques Wainer,et al.  Automatic fruit and vegetable classification from images , 2010 .

[4]  Vincent Leemans,et al.  Defects segmentation on 'Golden Delicious' apples by using colour machine vision , 1998 .

[5]  Vincent Leemans,et al.  A real-time grading method of apples based on features extracted from defects , 2004 .

[6]  Zhong Xie,et al.  The improved Douglas-Peucker algorithm based on the contour character , 2011, 2011 19th International Conference on Geoinformatics.

[7]  Robert M. Haralick,et al.  Textural Features for Image Classification , 1973, IEEE Trans. Syst. Man Cybern..

[8]  N. Otsu.  A threshold selection method from gray level histograms , 1979 .

[9]  R. R. Wolfe,et al.  Feature Extraction Techniques for Sorting Tomatoes by Computer Vision , 1985 .

[10]  Anjan Sarkar,et al.  An identification approach for 2-D autoregressive models in describing textures , 1991, CVGIP Graph. Model. Image Process..

[11]  R. N. Shebiah,et al.  Fruit Recognition using Color and Texture Features , 2010 .

[12]  Gary R. Bradski,et al.  ORB: An efficient alternative to SIFT or SURF , 2011, 2011 International Conference on Computer Vision.

[13]  John F. Canny,et al.  A Computational Approach to Edge Detection , 1986, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[14]  Ferat Sahin.  A Radial Basis Function Approach to a Color Image Classification Problem in a Real Time Industrial Application , 1997 .

[15]  Mario Cifrek,et al.  A brief introduction to OpenCV , 2012, 2012 Proceedings of the 35th International Convention MIPRO.

[16]  Jun Zhao,et al.  On-tree fruit recognition using texture properties and color data , 2005, 2005 IEEE/RSJ International Conference on Intelligent Robots and Systems.