Fruit bruise detection based on 3D meshes and machine learning technologies

This paper studies bruise detection in apples using 3-D imaging. Bruise detection based on 3-D imaging overcomes many limitations of bruise detection based on 2-D imaging, such as low accuracy, sensitive to light condition, and so on. In this paper, apple bruise detection is divided into two parts: feature extraction and classification. For feature extraction, we use a framework that can directly extract local binary patterns from mesh data. For classification, we studies support vector machine. Bruise detection using 3-D imaging is compared with bruise detection using 2-D imaging. 10-fold cross validation is used to evaluate the performance of the two systems. Experimental results show that bruise detection using 3-D imaging can achieve better classification accuracy than bruise detection based on 2-D imaging.

[1]  NanniLoris,et al.  Local binary patterns variants as texture descriptors for medical image analysis , 2010 .

[2]  C. Schmid,et al.  Description of Interest Regions with Center-Symmetric Local Binary Patterns , 2006, ICVGIP.

[3]  Yang Tao,et al.  Gabor feature-based apple quality inspection using kernel principal component analysis , 2007 .

[4]  Nikolas P. Galatsanos,et al.  A support vector machine approach for detection of microcalcifications , 2002, IEEE Transactions on Medical Imaging.

[5]  Stefanos Zafeiriou,et al.  Local normal binary patterns for 3D facial action unit detection , 2012, 2012 19th IEEE International Conference on Image Processing.

[6]  Shaogang Gong,et al.  Facial expression recognition based on Local Binary Patterns: A comprehensive study , 2009, Image Vis. Comput..

[7]  Ning Wang,et al.  Early detection of apple bruises on different background colors using hyperspectral imaging , 2008 .

[8]  D. L. Peterson,et al.  Identifying defects in images of rotating apples , 2005 .

[9]  Vladimir Vapnik,et al.  Statistical learning theory , 1998 .

[10]  J. Abbott,et al.  NEAR-INFRARED DIFFUSE REFLECTANCE FOR QUANTITATIVE AND QUALITATIVE MEASUREMENT OF SOLUBLE SOLIDS AND FIRMNESS OF DELICIOUS AND GALA APPLES , 2003 .

[11]  Renfu Lu,et al.  Detection of bruises on apples using near-infrared hyperspectral imaging , 2003 .

[12]  Yuming Zhao,et al.  Fast Tracking of Object Contour Based on Color and Texture , 2009, Int. J. Pattern Recognit. Artif. Intell..

[13]  Loris Nanni,et al.  Local binary patterns variants as texture descriptors for medical image analysis , 2010, Artif. Intell. Medicine.

[14]  Alberto Del Bimbo,et al.  The Mesh-LBP: Computing Local Binary Patterns on Discrete Manifolds , 2013, 2013 IEEE International Conference on Computer Vision Workshops.

[15]  Matti Pietikäinen,et al.  A comparative study of texture measures with classification based on featured distributions , 1996, Pattern Recognit..

[16]  Liming Chen,et al.  3D facial expression recognition via multiple kernel learning of Multi-Scale Local Normal Patterns , 2012, Proceedings of the 21st International Conference on Pattern Recognition (ICPR2012).

[17]  Xiaoyang Tan,et al.  Enhanced Local Texture Feature Sets for Face Recognition Under Difficult Lighting Conditions , 2007, AMFG.

[18]  G. M. Hyde,et al.  Non-contact bruise detection in apples by thermal imaging , 2003 .

[19]  M. Kohl,et al.  Near-infrared optical properties of ex vivo human skin and subcutaneous tissues measured using the Monte Carlo inversion technique. , 1998, Physics in medicine and biology.

[20]  Josse De Baerdemaeker,et al.  Combination of chemometric tools and image processing for bruise detection on apples , 2007 .

[21]  L.-X. Lu,et al.  Dropping Bruise Fragility and Bruise Boundary of Apple Fruit , 2007 .

[22]  Daniel E. Guyer,et al.  Near-infrared hyperspectral reflectance imaging for detection of bruises on pickling cucumbers , 2006, Computers and Electronics in Agriculture.

[23]  Pictiaw Chen,et al.  Detection of bruises in magnetic resonance images of apples , 1995 .

[24]  Christopher B. Watkins,et al.  A Quantitative and Qualitative Analysis of Antioxidant Enzymes in Relation to Susceptibility of Apples to Superficial Scald , 2003 .

[25]  Raimondo Schettini,et al.  3D face detection using curvature analysis , 2006, Pattern Recognit..

[26]  Matti Pietikäinen,et al.  Multiresolution Gray-Scale and Rotation Invariant Texture Classification with Local Binary Patterns , 2002, IEEE Trans. Pattern Anal. Mach. Intell..

[27]  M. Destain,et al.  Development of a multi-spectral vision system for the detection of defects on apples , 2005 .

[28]  Reyer Zwiggelaar,et al.  Use of Spectral Information and Machine Vision for Bruise Detection on Peaches and Apricots , 1996 .

[29]  U. M. Peiper,et al.  A Spectrophotometric Method for Detecting Surface Bruises on "Golden Delicious" Apples , 1994 .

[30]  Xiaoyang Tan,et al.  Enhanced Local Texture Feature Sets for Face Recognition Under Difficult Lighting Conditions , 2007, IEEE Transactions on Image Processing.