Hyperspectral Imaging and Nonlinear Fukunaga-Koontz Transform Based Food Inspection

Nowadays, food safety is a great public concern;
therefore, robust and effective techniques are required for detecting
the safety situation of goods. Hyperspectral Imaging (HSI) is an
attractive material for researchers to inspect food quality and safety
estimation such as meat quality assessment, automated poultry
carcass inspection, quality evaluation of fish, bruise detection of
apples, quality analysis and grading of citrus fruits, bruise detection
of strawberry, visualization of sugar distribution of melons,
measuring ripening of tomatoes, defect detection of pickling
cucumber, and classification of wheat kernels. HSI can be used to
concurrently collect large amounts of spatial and spectral data on the
objects being observed. This technique yields with exceptional
detection skills, which otherwise cannot be achieved with either
imaging or spectroscopy alone. This paper presents a nonlinear
technique based on kernel Fukunaga-Koontz transform (KFKT) for
detection of fat content in ground meat using HSI. The KFKT which
is the nonlinear version of FKT is one of the most effective
techniques for solving problems involving two-pattern nature. The
conventional FKT method has been improved with kernel machines
for increasing the nonlinear discrimination ability and capturing
higher order of statistics of data. The proposed approach in this paper
aims to segment the fat content of the ground meat by regarding the
fat as target class which is tried to be separated from the remaining
classes (as clutter). We have applied the KFKT on visible and nearinfrared
(VNIR) hyperspectral images of ground meat to determine
fat percentage. The experimental studies indicate that the proposed
technique produces high detection performance for fat ratio in ground
meat.

Authors:

• Hamidullah Binol
• Abdullah Bal

Keywords:

• Food (Ground meat) inspection
• Fukunaga-Koontz transform
• hyperspectral imaging
• kernel methods.

References:

[1] A. A. Gowen, C. P. O’Donnell, P. J. Cullen, G.Downey, and J. M. Frias, “Hyperspectral imaging - an emerging process analytical tool for food quality and safety control,” Trends in Food Science &Technology, vol. 18, pp. 590–598, 2007.
[2] R. W. Basedow, D. C. Carmer, and M. L. Anderson, “HYDICE System, Implementation and Performance,” SPIE Proc., vol. 2480, pp. 258-267, Orlando, FL, 17-18 April 1995.
[3] N. Gupta, and R. Dahmani, “Multispectral and hyperspectral imaging with AOTF for object recognition,” The 27th AIPR Workshop: Advances in Computer-Assisted Recognition. International Society for Optics and Photonics, pp. 128-135, 1999.
[4] D. Manolakis, D. Marden, and G. A. Shaw, “Hyperspectral image processing for automatic target detection applications,” Lincoln Laboratory Journal, vol. 14, no. 1, pp. 79-116, 2003.
[5] G. P. Petropoulos, C. Kalaitzidis, and K. P. Vadrevu, “Support vector machines and object-based classification for obtaining land-use/cover cartography from Hyperion hyperspectral imagery,” Computers & Geosciences, vol. 41, pp. 99-107, 2012.
[6] C. Balas, “Review of biomedical optical imaging—a powerful, non-invasive, non-ionizing technology for improving in vivo diagnosis,” Measurement science and technology, vol. 20, no. 10, 104020, 2009.
[7] Y. Z. Feng, and D. W. Sun, “Application of hyperspectral imaging in food safety inspection and control: a review,” Critical reviews in food science and nutrition, vol. 52, no. 11, pp. 1039-1058, 2012.
[8] H. Huang, L. Liu, and M. O. Ngadi, “Recent developments in hyperspectral imaging for assessment of food quality and safety,” Sensors, vol. 14, no. 4, pp. 7248-7276, 2014.
[9] R. Liu, E. Liu, J. Yang, T. Zhang, and F. Wang, “Infrared small target detection with kernel Fukunaga-Koontz transform,” Meas. Sci. Technol, vol. 18, no. 9, pp. 3025-3035, 2007.
[10] H. Binol, G. Bilgin, S. Dinc, and A. Bal, “Kernel Fukunaga-Koontz Transform Subspaces for Classification of Hyperspectral Images with Small Sample Sizes,” IEEE Geosci. and Rem. Sens. Let., vol. 12, no. 6, pp. 1287-1291, 2015.
[11] Y-H. Li, and M. Savvides, “Kernel Fukunaga-Koontz transform subspaces for enhanced face recognition,” in IEEE Conf. on Computer Vision and Pattern Recognition (CVPR 2007), pp. 1-8, June 2007.
[12] X. Zhang, F. Liu, Y. He, and X. Li, “Application of hyperspectral imaging and chemometric calibrations for variety discrimination of maize seeds,” Sensors, vol. 12, no. 12, pp. 17234-17246, 2012.
[13] H. Grahn, and P. Geladi, eds. Techniques and applications of hyperspectral image analysis. John Wiley & Sons, 2007.
[14] K. Fukunaga and W. L. G. Koontz, “Applications of the Karhunen-Loève expansion to feature selection and ordering,” IEEE Transaction on Computers, vol.19, no. 5, pp. 311–318, 1970.
[15] S. R. F. Sims, and A. Mahalanobis, “Performance evaluation of quadratic correlation filters for target detection and discrimination in infrared imagery,” Optical Engineering, vol. 43, pp. 1705 -1711, 2004.
[16] M. Aizerman, E. Braverman, and L. Rozonoer, “Theoretical foundations of the potential function method in pattern recognition learning,” Automation and Remote Control, vol. 25, pp. 821-837, 1964.
[17] J. Shawe-Taylor, and N. Cristianini, Kernel Methods for Pattern Analysis. Cambridge: Cambridge University Press, 2004.
[18] X. Huo, et al., “Optimal Reduced-Rank Quadratic Classifiers using the Fukunaga-Koontz Transform with Applications to Automatic Target Recognition,” Proceedings of SPIE, vol. 5094, 2003.
[19] J. Yang, A. F. Frangi, J. Y. Yang, D. Zhang, and Z. Jin, Z, “KPCA plus LDA: a complete kernel Fisher discriminant framework for feature extraction and recognition,” Pattern Analysis and Machine Intelligence, IEEE Transactions on, vol. 27, no. 2, pp. 230-244, 2005.
[20] H. Binol , A. Bal, and H. Cukur, “Differential evolution algorithm-based kernel parameter selection for Fukunaga-Koontz Transform subspaces construction,” Proc. SPIE, High-Performance Computing in Remote Sensing V, vol. 9646, October 2015.