Due to the increasing consumption of food products and demand for food quality and safety, most food processing facilities in the United States utilize machines to automate their processes, such as cleaning, inspection and grading, packing, storing, and shipping. Machine vision technology has been a proven solution for inspection and grading of food products since the late 1980s. The remaining challenges, especially for small to midsize facilities, include the system and operating costs, demand for high-skilled workers for complicated configuration and operation and, in some cases, unsatisfactory results. We developed an embedded solution with learning capability to alleviate these challenges.
3. Z.H. Guo, M. Zhang, D.J. Lee, and T.S. Simons, "Smart Camera for Quality Inspection and Grading of Food Products", Electronics, vol. 9(3), pp. 505-18pages, March 2020.
2. Z.H. Guo, M. Zhang, and D.J. Lee, "Efficient Evolutionary Learning Algorithm for Real-Time Embedded Vision Applications", Electronics,vol. 8(11), pp.1367-18 pages, November 2019.
1. T.S. Simons and D.J. Lee, "Jet Features: Hardware-Friendly, Learned Convolutional Kernels for High-Speed Image Classification", Electronics, vol. 8(5), pp. 588-20 pages, May 2019.
Many quality evaluation tasks that are complicated and unique to produce and food products are often carried out manually by human experts by visually inspecting product appearances. This labor-intensive process usually depends greatly on experienced workers and lacks verification efficiency. Automating these tasks not only reduces the processing time, improves the verification accuracy, but also reduces the labor costs. Color and surface texture are two very important grading criteria for the food and produce industries.
7. M. Zhang, D.J. Lee, K.D. Lillywhite, and B.J. Tippetts, "Automatic Quality and Moisture Evaluations Using Evolution Constructed Features,” Computers and Electronics in Agriculture, vol. 135, p. 321-327, April 2017.
6. D. Zhang, D.J. Lee, B.J. Tippetts, and K.D. Lillywhite, "Date Quality Evaluation Using Short-wave Infrared Imaging”, Journal of Food Engineering, vol. 141, p. 74-84, November 2014.
5. D. Zhang, D.J. Lee, B.J. Tippetts, and K.D. Lillywhite, "Date Maturity and Quality Evaluation Using Color Distribution Analysis and Back Projection”, Journal of Food Engineering, vol. 131, p. 161-169, June 2014.
4. D. Zhang, K.D. Lillywhite, D.J. Lee, and B.J. Tippetts, "Automated Apple Stem End and Calyx Detection using Evolution-COnstructed Features,” Journal of Food Engineering, vol. 119/3, p. 411-418, December 2013.
3. D.J. Lee, J.K Archibald, and G.M. Xiong, "Rapid Color Grading for Fruit Quality Evaluation Using Direct Color Mapping”, IEEE Transactions on Automation Science and Engineering, vol. 8/2, p. 292-302, April 2011.
2. D.J. Lee, J.K. Archibald, Y.C. Chang, and C.R. Greco, "Robust Color Space Conversion and Color Distribution Analysis Techniques for Date Maturity Evaluation”, Journal of Food Engineering, vol. 88/3, p. 364-372, October 2008
1. D.J. Lee, R.B. Schoenberger, J.K. Archibald, and S.P. McCollum, "Development of a Machine Vision System for Automatic Date Grading Using Digital Reflective Near-Infrared Imaging”, Journal of Food Engineering, vol. 86/3, p. 388-398, June 2008.
Shape and size are important grading criteria for visual inspection automation. A simple and accurate shape analysis method was developed for size and shape-based oyster grading. The algorithm is was first programmed to perform shape analysis for fish species recognition and later expanded to grade oysters and shrimp.
4. G.M. Xiong, D.J. Lee, K.R. Moon, and R.M. Lane, "Shape Similarity Measure Using Turn Angle Cross-correlation for Oyster Quality Evaluation”, Journal of Food Engineering, vol. 100/1, p. 178-186, September 2010.
3. G.H. Chang, G.J. Kerns, D.J. Lee, and G.L. Stanek, "Calibration Experiments for a Computer Vision Oyster Volume Estimation System”, Journal of Statistics Education, vol. 17/2, 20 pages, July 2009.
2. X. Xi, K. Ueno, E. Keogh, and D.J. Lee, "Converting Non-parametric Distance-Based Classification to Anytime Algorithms”, special issue on “Non-parametric Distance-based Classification Techniques and their Applications” of Pattern Analysis and Applications, vol. 11/3-4, p. 321-336, September 2008.
1. D.J. Lee, J.K. Archibald, X.Q. Xu, and P.C. Zhan, "Using Distance Transform to Solve Real-time Machine Vision Inspection Problems”, Machine Vision and Applications Journal, vol. 18/2, p. 85-93, April 2007.
Surface area and volume measurements provide important information for agriculture and food-processing applications. We sued a nondestructive method to measure volume and
surface area of objects with irregular shapes. The system first takes a series of silhouettes of the object from different directions by rotating the object at fixed angular intervals. The boundary points of each image are then extracted to construct a silhouette. A three dimensional wire-frame model of the object can be reconstructed by integrating silhouettes obtained from different view angles.
4. D.J. Lee, J.K. Archibald, X.Q. Xu, and P.C. Zhan, "Using Distance Transform to Solve Real-time Machine Vision Inspection Problems”, Machine Vision and Applications Journal, vol. 18/2, p. 85-93, April 2007.
3. J.D. Eifert, G.C. Sanglay, D.J. Lee, S.S. Sumner, and M.D. Pierson, "Prediction of Raw Produce Surface Area from Weight Measurement”, Journal of Food Engineering, vol. 7/4, p. 552-556, June 2006.
2. D.J. Lee, X.Q. Xu, J.D. Eifert, and P. Zhan, "Area and Volume Measurements of Objects with Irregular Shapes Using Multiple Silhouettes”, Optical Engineering, vol. 45/2, p. 027202-27212, February 2006.
1. D.J. Lee, J.D. Eifert, P.C. Zhan, and B.P. Westover, "Fast Surface Approximation for Volume and Surface Area Measurements Using Distance Transform”, Optical Engineering, vol. 42/10, p. 2947-2955, October 2003.