Nondestructive grading of black tea based on physical parameters by texture analysis

Texture is an important characteristic used in identification of objects or regions of interest in an image. This paper describes a technique to discriminate between four different grades of made black tea using textural features based on grey tone spatial dependencies. The statistical features were computed from the tea images and wavelet decomposed sub band images. The multi-layer perceptron (MLP) technique has been used for data classification and 82.33% classification accuracy was achieved. Finally, statistical analysis in the form of one way analysis of variance (ANOVA) has been employed as a validation tool to check for grading accuracy.

[1]  Chun-Shien Lu,et al.  Unsupervised texture segmentation via wavelet transform , 1997, Pattern Recognit..

[2]  F A Payne,et al.  Application of image texture analysis for online determination of curd moisture and whey solids in a laboratory-scale stirred cheese vat. , 2008, Journal of food science.

[3]  A. Gagalowicz,et al.  2‐D Macroscopic Texture Synthesis , 1989 .

[4]  I. Daubechies.  Orthonormal bases of compactly supported wavelets , 1988 .

[5]  Anil K. Jain,et al.  Texture Segmentation Using Voronoi Polygons , 1990, IEEE Trans. Pattern Anal. Mach. Intell..

[6]  Da-Wen Sun,et al.  Correlation between Cheese Meltability Determined with a Computer Vision Method and with Arnott and Schreiber Tests , 2002 .

[7]  Amod Kumar,et al.  Monitoring and grading of tea by computer vision – A review , 2011 .

[8]  J. Daugman.  Uncertainty relation for resolution in space, spatial frequency, and orientation optimized by two-dimensional visual cortical filters. , 1985, Journal of the Optical Society of America. A, Optics and image science.

[9]  Vidya B. Manian,et al.  Scaled and rotated texture classification using a class of basis functions , 1998, Pattern Recognit..

[10]  Da-Wen Sun,et al.  Inspection and grading of agricultural and food products by computer vision systems—a review , 2002 .

[11]  Manabendra Bhuyan,et al.  Non-destructive testing of tea fermentation using image processing , 2003 .

[12]  Geoffrey E. Hinton,et al.  Learning internal representations by error propagation , 1986 .

[13]  James L. McClelland,et al.  Parallel distributed processing: explorations in the microstructure of cognition, vol. 1: foundations , 1986 .

[14]  Jian Fan,et al.  Texture Classification by Wavelet Packet Signatures , 1993, MVA.

[15]  Hideyuki Tamura,et al.  Textural Features Corresponding to Visual Perception , 1978, IEEE Transactions on Systems, Man, and Cybernetics.

[16]  Abdulrahman Al-Janobi,et al.  Performance evaluation of cross-diagonal texture matrix method of texture analysis , 2001, Pattern Recognit..

[17]  A. Koutsoyiannis.  Theory of Econometrics , 1974 .

[18]  Mridul Hazarika,et al.  Chlorophylls and degradation products in orthodox and CTC black teas and their influence on shade of colour and sensory quality in relation to thearubigins , 1985 .

[19]  Azriel Rosenfeld,et al.  A Comparative Study of Texture Measures for Terrain Classification , 1975, IEEE Transactions on Systems, Man, and Cybernetics.

[20]  Michael Unser,et al.  Texture classification and segmentation using wavelet frames , 1995, IEEE Trans. Image Process..

[21]  E. Hines,et al.  Wavelet transform based image texture analysis for size estimation applied to the sorting of tea granules , 2007 .

[22]  Ezzatollah Salari,et al.  Texture segmentation using hierarchical wavelet decomposition , 1995, Pattern Recognit..

[23]  S. Singh,et al.  Evaluation of texture methods for image analysis , 2001, The Seventh Australian and New Zealand Intelligent Information Systems Conference, 2001.

[24]  Da-Wen Sun,et al.  Recent advances in image processing using image texture features for food quality assessment , 2013 .

[25]  Stéphane Mallat,et al.  Multifrequency channel decompositions of images and wavelet models , 1989, IEEE Trans. Acoust. Speech Signal Process..

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

[27]  Wilson S. Geisler,et al.  Multichannel Texture Analysis Using Localized Spatial Filters , 1990, IEEE Trans. Pattern Anal. Mach. Intell..

[28]  J. Macgregor,et al.  Image texture analysis: methods and comparisons , 2004 .