Biscuits and cookies are one of the major parts of Indian bakery products. The bake level of biscuits and cookies is of significant value to various bakery products as it determines the taste, texture, number of chocolate chips, uniformity in distribution of chocolate chips, and various features related to appearance of products. Six threshold methods (isodata, Otsu, minimum error, moment preserving, Fuzzy, manual method, and k-mean clustering) have been implemented for chocolate chips extraction from captured cookie image. Various other image processing operations such as entropy calculation, area calculation, parameter calculation, baked dough color, solidity, and fraction of top surface area have been implemented for commercial KrackJack biscuits and cookies. Proposed algorithm is able to detect and investigate about various defects such as crack and various spots. A simple and low cost machine vision system with improved version of robust algorithm for quality detection and identification is envisaged. Developed system and robust algorithm have a great application in various biscuit and cookies baking companies. Proposed system is composed of a monochromatic light source, and USB based 10.0 megapixel camera interfaced with ARM-9 processor for image acquisition. MATLAB version 5.2 has been used for development of robust algorithms and testing for various captured frames. Developed methods and procedures were tested on commercial biscuits resulting in the specificity and sensitivity of more than 94% and 82%, respectively. Since developed software package has been tested on commercial biscuits, it can be programmed to inspect other manufactured bakery products. 1. Introduction Automating visual inspection based quality control processes are highly desirable for manufacturing companies as it significantly reduces manufacturing costs and can provide for greater accuracy in the monitoring of their manufacturing processes for different biscuits varieties [1]. Physical features present on cookies can be examined by employing various image processing algorithms to extract various features of importance [2]. A wide array of image acquisition technology is available that helps make this automation efficient and cost effective [3]. Developed algorithm also greatly simplifies the image processing techniques used in an automation process by handling at acquisition level common problems such as light reflection and color variation in an acquired image. Various physical parameters have been extracted using developed algorithm such as convex area, solidity, change
References
[1]
T. Yamaguchi, S. Nakamura, R. Saegusa, and S. Hashimoto, “Image-based crack detection for real concrete surfaces,” IEEJ Transactions on Electrical and Electronic Engineering, vol. 3, no. 1, pp. 128–135, 2008.
[2]
Q. Li, W. Lu, and J. Yang, “A hybrid thresholding algorithm for cloud detection on ground-based color images,” Journal of Atmospheric and Oceanic Technology, vol. 28, no. 10, pp. 1286–1296, 2011.
[3]
S. Nashat, A. Abdullah, and M. Z. Abdullah, “A robust crack detection method for non-uniform distributions of coloured and textured image,” in Proceedings of the IEEE International Conference on Imaging Systems and Techniques (IST '11), pp. 98–103, Penang, Malaysia, 2011.
[4]
W.-T. Chang, “Chocolate Chips Counting by Multiple Image Operation,” EECS 90 Digital Image Processing, 2004.
[5]
J. Eastman, “Automated Tallying of Chocolate Chips using Color Segmentation,” EECS 90 Digital Image Processing, 2004.
[6]
C. Roberts and F. Merat, “Surface Feature Detection for Quality Assurance with Cookies,” EECS 90 Digital Image Processing, 2004.
[7]
S. Johannsen, “Counting Chocolate Chips in Chocolate Chip Cookies,” EECS 90 Digital Image Processing, 2004.
[8]
V. Guillard, B. Broyart, S. Guilbert, C. Bonazzi, and N. Gontard, “Moisture diffusivity and transfer modelling in dry biscuit,” Journal of Food Engineering, vol. 64, no. 1, pp. 81–87, 2004.
[9]
J. C. H. Yeh and L. G. C. Hamey, “Biscuit bake assessment by an artificial neural network,” in Proceedings of the 5th Australian Conference on Artificial Neural Networks, 1994.
[10]
M. Z. Abdullah, L. C. Guan, and B. M. N. Mohd Azemi, “Stepwise discriminant analysis for colour grading of oil palm using machine vision system,” Food and Bioproducts Processing, vol. 79, no. 4, pp. 223–231, 2001.
[11]
K. Ganesan, G. Abraham, and S. K. Jayan, “A machine vision system for biscuit classification and fault detection,” International Journal of Applied Engineering Research, vol. 8, no. 19, 2013.
[12]
K. Woertz, C. Tissen, P. Kleinebudde, and J. Breitkreutz, “Taste sensing systems (electronic tongues) for pharmaceutical applications,” International Journal of Pharmaceutics, vol. 417, no. 1-2, pp. 256–271, 2011.
[13]
F. R. Dias Velasco, “Thresholding using the ISODATA clustering algorithm,” IEEE Transactions on Systems, Man, and Cybernetics, vol. 10, no. 11, pp. 771–774, 1980.
[14]
O. Nobuyuki, “A threshold selection method from gray-level histograms,” IEEE Transactions on Systems, Man, and Cybernetics, vol. 9, no. 1, pp. 62–66, 1979.
[15]
N. Otsu, “A threshold selection method from gray-level on histograms,” IEEE Transactions on Systems, Man and Cybernetics, vol. 9, no. 1, pp. 62–66, 1979.
[16]
J. Kittler and J. Illingworth, “Minimum error thresholding,” Pattern Recognition, vol. 19, no. 1, pp. 41–47, 1986.
[17]
T. Kanungo, D. M. Mount, N. S. Netanyahu, C. D. Piatko, R. Silverman, and A. Y. Wu, “An efficient k-means clustering algorithms: analysis and implementation,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 24, no. 7, pp. 881–892, 2002.
[18]
C. V. Jawahar, P. K. Biswas, and A. K. Ray, “Investigations on fuzzy thresholding based on fuzzy clustering,” Pattern Recognition, vol. 30, no. 10, pp. 1605–1613, 1997.
[19]
J. C.-H. Yeh, L. G. C. Hamey, T. Westcott, and S. K. Y. Sung, “Colour bake inspection system using hybrid artificial neural networks,” in Proceedings of the IEEE International Conference on Neural Networks, vol. 1, pp. 37–42, Perth, Australia, 1995.
[20]
T. B. Moeslund and E. Granum, “A survey of computer vision-based human motion capture,” Computer Vision and Image Understanding, vol. 81, no. 3, pp. 231–268, 2001.
[21]
U. Gonzales-Barron and F. Butler, “A comparison of seven thresholding techniques with the k-means clustering algorithm for measurement of bread-crumb features by digital image analysis,” Journal of Food Engineering, vol. 74, no. 2, pp. 268–278, 2006.
[22]
V. N. Manjunath, G. A. Kumar, and P. Shivakumara, “Skew detection technique for binary document images based on Hough transform,” International Journal of Information and Communication Engineering, vol. 3, pp. 498–504, 2007.
[23]
D. Bertand, C. Leguerneve, D. Marion, M. F. Devaux, and P. Robert, “Description of the textural appearance of bread crumb by vedio image analysis,” Cereal Chemistry, vol. 69, no. 3, pp. 257–261, 1992.
[24]
Y. Han and P. Shi, “An adaptive level-selecting wavelet transform for texture defect detection,” Image and Vision Computing, vol. 25, no. 8, pp. 1239–1248, 2007.
