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Meeting Time and Place:<\/strong><\/p>\n Instructor:<\/strong>\u00a0 Manoj Karkee<\/p>\n \u00b7\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 Office Hours: Friday 3:30PM to 4:30PM or by appointment (remote students can reach me via Skype, Whatsapp, Viber, Wechat or any medium of communication as preferred; ID will be provided)<\/p>\n \u00b7\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 Office: 105 Ag Tech Building<\/p>\n \u00b7\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 Phone:\u00a0 509-786-9208<\/p>\n \u00b7\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 Email: manoj.karkee@wsu.edu<\/a><\/p>\n <\/p>\n Objectives:<\/strong><\/p>\n The major objective of BSysE 530 is to help students understand and apply image processing techniques and machine vision systems to solve engineering and scientific problems of their interest. Most of the examples and homework problems will be related to agricultural and biological systems. However, the concept learned in this class can be applied to solve a wide range of problems in various disciplines including science, engineering and medicine.<\/p>\n <\/p>\n Prerequisites:<\/strong><\/p>\n It is anticipated that the students have taken two or more undergraduate level math classes including exposure to calculus and linear algebra. It is also expected that the students have some programming background and\/or interest to learn Matlab-based programming.<\/p>\n <\/p>\n Required Texts:<\/strong><\/p>\n <\/p>\n Required Reading <\/strong>(Each Wednesday, one student will be randomly selected for brief discussion on the reading from previous week):<\/p>\n \u00a0<\/strong><\/p>\n Week#01: Aloimonos and Rosenfeld, 1991. Computer Vision. Science, 253\u00a0(5025): 1249-1254\u00a0– Sep 1991.<\/p>\n Week#02_01: Sistler, 1987. Robotics and intelligent machines in agriculture. IEEE Journal of Robotics and Automation, 3(1):3-6.<\/p>\n Week#02_02: Narendra and Hareesh, 2010. Prospects of computer vision, automated grading and sorting systems in agricultural and food products for quality evaluation. International Journal of Computer Applications, 1(4): 1-9.<\/p>\n Week#03: Gongal, A., S. Amatya, M. Karkee, Q. Zhang, and K. Lewis. 2014. Sensors and Systems for Fruit Detection and Localization: A Review. Computers and Electronics in Agriculture, 116:8-19.<\/p>\n Week#04: Milella, Reina and Foglia, 2006. Computer vision technology for agricultural robotics. Sensor Review, 24(4): 290-300.<\/p>\n Week#05: Bolle, Connell, Haas, Mohan and Taubin, 1996. Veggie vision: a produce recognition system. WACV 1996. IEEE.<\/p>\n Week#06:\u00a0Mehl, Chen, Kim and Chan. 2004. Development of hyperspectral imaging technique for the detection of apple surface defects and contaminations. Journal of Food Engg, 61: 67-81.<\/p>\n Week#07: Leemans, Megein and Destain, 2002. On-line fruit grading according to their external quality using machine vision. Biosystems Engineering, 83(4): 397-404.<\/p>\n Week#08: Lottes, P., H\u00f6rferlin, M., Sander, S., & Stachniss, C. (2017). Effective Vision\u2010based Classification for Separating Sugar Beets and Weeds for Precision Farming. Journal of Field Robotics, 34(6): 160-178.<\/p>\n Week#09: Bargoti, S. and Underwood, J.P., 2017. Image segmentation for fruit detection and yield estimation in apple orchards. Journal of Field Robotics.<\/p>\n Week#10: Reid, Zhanga<\/a>,\u00a0 Noguchi,\u00a0 Dickson, 2000. Agricultural automatic guidance research in North America. Computers and Electronics in Agriculture, 25(2): 155-167.<\/p>\n Week#11:\u00a0Week#11: Shrestha, D. S., Steward, B. L., & Birrell, S. J. (2004). Video processing for early stage maize plant detection. Biosystems engineering, 89(2), 119-129.<\/p>\n Week#13: A Method for Three Dimensional Reconstruction of Apple Trees for Automated Pruning. Transactions of the ASABE<\/em>, 58(3): 565-574.<\/p>\n \u00a0<\/strong><\/p>\n References:<\/strong><\/p>\n <\/p>\n Matlab References:<\/strong><\/p>\n \u00a0<\/strong><\/p>\n Relevant Journals:<\/strong><\/p>\n <\/p>\n Course Assignments:<\/strong><\/p>\n Homework: <\/u><\/em>\u00a0Seven homework assignments have been scheduled for the semester.\u00a0 Some homework assignments need formal written report, others need just well-commented code and results. Please see the schedule to find out which homework requires a formal report. Suggested format for the formal report will be provided. Students may report in a different format as long as all necessary information is included.<\/p>\n Class Project: <\/u><\/em>\u00a0You are required to prepare an individual project proposal with a clearly identified need or problem that will be addressed through the techniques and algorithms learned in this course. Your project will be presented to the class at end of the semester.\u00a0 A written project proposal and a final report will also be required. Format will be provided.<\/p>\n Exams: <\/u><\/em>There will be NO formal exams. It is expected that students will have learned enough material to complete homework assignments and class project.<\/p>\n \u00a0<\/strong><\/p>\n Web Page and E-mail: <\/strong><\/p>\n The course has a Blackboard Page (https:\/\/learn.wsu.edu<\/a>) where you can find all the class materials. Code and results for all the assignments and soft copy of formal reports will be uploaded to Blackboard page. Only<\/em> if there is a technical problem to work on Blackboard, these materials can be submitted to manoj.karkee@wsu.edu<\/a> with subject line including keywords BSysE530<\/em> and\/or Machine Vision<\/em>.<\/p>\n Basic course materials is also available at https:\/\/labs.wsu.edu\/karkee-ag-robotics\/machinevision\/<\/p>\n Grading:<\/strong><\/p>\n\n
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