The field of computer vision has come a long way in solving the problem of image classification. Not too long ago, handcrafted convolutional kernels were a staple of all computer vision algorithms. With the advent of Convolutional Neural Networks (CNNs), however, handcrafted features have become the exception rather than the rule, and for good reason. CNNs have taken the field of computer vision to new heights by solving problems that used to be unapproachable or unthinkable. Robotic Vision Lab focuses on using CNNs to solve real-world problems beyond object recognition and image classification. Another research focus is the optimization and hardware implementation of CNNs for real-time embedded applications.
5. Y.Y. Li, D. Zhang, and D. J. Lee , "Automatic Fabric Defect Detection with a Wide-And-Compact Network", Journal of Neurocomputing, vol. 329, pp. 329-338, February 2019.
4. Y.Y. Li, D. Zhang, and D. J. Lee, "IIRNet: A Lightweight Deep Neural Network Using Intensely Inverted Residuals for Image Recognition", Image and Vision Computing, vol. 92, Article# 103819, December 2019.
3. T.S. Simons and D.J. Lee, "A Review of Binarized Neural Networks", Electronics, vol. 8(6), pp. 661-25 pages, June 2019.
2. 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.
1. J.N. Teng, D. Zhang, D.J. Lee, "Recognition of Chinese Food Using Convolutional Neural Network", Multimedia Tools and Applications, vol. 78(9), pp. 11155-11172, May 2019.
Object recognition is a well studied but extremely challenging field. We developed a novel approach to feature construction for object detection called Evolution-COnstructed Features (ECO features) in 2010. ECO features are automatically constructed by uniquely employing a standard genetic algorithm to discover multiple series of transforms that are highly discriminative. We have successfully applied this algorithm to many visual inspection applications including automated apple stem and calyx detection, shrimp shape quality grading, fish species recognition, invasive carp removal, fruit and food quality grading, and road condition and pavement quality evaluation. Demo Video.
9. 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.
8. 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.
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, M. Zhang. B.J. Tippetts, and K.D. Lillywhite, "Object Recognition Algorithm for the Automatic Identification and Removal of Invasive Fish,” Biosystems Engineering, vol. 145, p. 65-75, May 2016.
5. D. Zhang, K.D. Lillywhite, D.J. Lee, and B.J. Tippetts, "Automated Fish Taxonomy using Evolution-COnstructed Features for Invasive Species Removal”, Pattern Analysis and Applications, vol. 18/2. p. 451-459, May 2015.
4. D. Zhang, K.D. Lillywhite, D.J. Lee, and B.J. Tippetts, "Automatic Shrimp Shape Grading Using Evolution Constructed Features”, Computers and Electronics in Agriculture, vol. 100, p. 116-122, January 2014.
3. K.D. Lillywhite, D.J. Lee, B.J. Tippetts, and J.K Archibald, "A Feature Construction Method for General Object Recognition”, Pattern Recognition, vol. 46/12, p. 3300-3314, December 2013.
2. 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.
1. K.D. Lillywhite, B.J. Tippetts, and D.J. Lee, "Self-Tuned Evolution-COnstructed Features for General Object Recognition”, Pattern Recognition, vol. 45/1, p. 241-251, January 2012.
Vertebra shape can effectively describe various pathologies found in spine x-ray images. Some critical regions on the shape contour help determine whether the shape is pathologic or normal. We developed a technique to automatically select nine points from the boundary contour, represent vertebra shape using multiple open triangles, and relevance feedback to retrieve vertebra X-ray images.
4. D.J. Lee, S.K. Antani, Y.C. Chang, K. Gledhill, L.R. Long, and P. Christensen, "CBIR of Spine X-ray Images on Inter-vertebral Disc Space and Shape Profiles”, special issue on "Knowledge Discovery in Medicine", Data & Knowledge Engineering Journal, vol. 68/12, p. 1359-1369, December 2009.
3. X.Q. Xu , D.J. Lee, S.K. Antani, L.R. Long, and J. K Archibald, " Using Relevance Feedback with Short-term Memory for Content-based Spine X-ray Image Retrieval”, Journal of Neurocomputing , vol. 72/10-12, p. 2259-2269, June 2009.
2. X.Q. Xu , D.J. Lee, S.K. Antani, and L.R. Long, "A Spine X-ray Image Retrieval System Using Partial Shape Matching”, IEEE Transactions on Information Technology in Biomedicine, vol. 12/1, p. 100-108, January 2008.
1. S.K. Antani, D.J. Lee, L.R. Long, and G.R. Thoma, "Evaluation of Shape Similarity Measurement Methods for Spine X-Ray Images”, Special issue on "Multimedia Database Management Systems" of the Journal of Visual Communication and Image Representation, vol. 15/3, p. 285-302, September 2004.