Spring 2018 Course Policy and Syllabus
Meeting Time and Place:
- Class meets on Wednesday and Friday at 2:10PM to 3:25PM in Room 125 Ag Tech Building, Center for Precision and Automated Agricultural Systems (CPAAS) – Prosser, WA and via Telecommunications (AMS) to Pullman and TriCities
Instructor: Manoj Karkee
· 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)
· Office: 105 Ag Tech Building
· Phone: 509-786-9208
· Email: email@example.com
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.
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.
-  L. Shapiro and G. Stockman, Computer Vision. Prentice-Hall. 2001.
-  Gonzalez, Woods and Eddins, Digital Image Processing with Matlab.
Required Reading (Each Wednesday, one student will be randomly selected for brief discussion on the reading from previous week):
Week#01: Aloimonos and Rosenfeld, 1991. Computer Vision. Science, 253 (5025): 1249-1254 – Sep 1991.
Week#02_01: Sistler, 1987. Robotics and intelligent machines in agriculture. IEEE Journal of Robotics and Automation, 3(1):3-6.
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.
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.
Week#04: Milella, Reina and Foglia, 2006. Computer vision technology for agricultural robotics. Sensor Review, 24(4): 290-300.
Week#05: Bolle, Connell, Haas, Mohan and Taubin, 1996. Veggie vision: a produce recognition system. WACV 1996. IEEE.
Week#06: Mehl, 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.
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.
Week#08: Lottes, P., Hörferlin, M., Sander, S., & Stachniss, C. (2017). Effective Vision‐based Classification for Separating Sugar Beets and Weeds for Precision Farming. Journal of Field Robotics, 34(6): 160-178.
Week#09: Bargoti, S. and Underwood, J.P., 2017. Image segmentation for fruit detection and yield estimation in apple orchards. Journal of Field Robotics.
Week#10: Reid, Zhanga, Noguchi, Dickson, 2000. Agricultural automatic guidance research in North America. Computers and Electronics in Agriculture, 25(2): 155-167.
Week#11: Week#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.
Week#13: A Method for Three Dimensional Reconstruction of Apple Trees for Automated Pruning. Transactions of the ASABE, 58(3): 565-574.
- Jain, R. Kasturi and B. Schunck, Machine Vision,McGraw Hill, 1995
- Forsyth and Ponce, Computer Vision: a modern approach, Prentice-Hall 2002 (available online at University of Washington homepage).
- Gonzalez and R. Woods, Digital Image Processing, 3rd edition, Prentice Hall, 2008.
- Trucco and A. Verri, Introductory Techniques for 3-D Computer Vision, Prentice Hall, 1998.
- Kecman, Learning and Soft Computing (Available for online reading through WSU library)
- Pratap, Getting Started with MATLAB 7: A Quick Introduction for Scientists and Engineers, Oxford University Press.
- Matlab Online Resources.
- Transactions of the ASABE
- Computers and Electronics in Agriculture
- Biosystems Engineering
- International Journal of Computer Vision
- IEEE Workshop on Applications of Computer Vision
- IEEE Journal of Robotics and Automation
- IEEE International Conference on Intelligent Robots and Systems (IROS)
- Journal of Field Robotics
Homework: Seven homework assignments have been scheduled for the semester. 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.
Class Project: You 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. A written project proposal and a final report will also be required. Format will be provided.
Exams: There will be NO formal exams. It is expected that students will have learned enough material to complete homework assignments and class project.
Web Page and E-mail:
The course has a Blackboard Page (https://learn.wsu.edu) 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 if there is a technical problem to work on Blackboard, these materials can be submitted to firstname.lastname@example.org with subject line including keywords BSysE530 and/or Machine Vision.
Basic course materials is also available at https://labs.wsu.edu/karkee-ag-robotics/machinevision/
|Component||Percent of Final Grade|
|Final Project Presentation||15% (Partly Peer Evaluation)|
|Final Project Report||20%|
Expected breakdown of the grade is as follows
Score At least a letter grade of:
≥ 90 A-
≥ 80 B-
≥ 70 C-
≥ 60 D-
Homework and Project Assignments: Homework are due at 11:59 on the due date indicated on the assignment. Late homework/assignment may receive a 10 % reduction for each day it is late, with no credit given to the assignment handed in after subsequent homework is due.
Grading disagreements: Any disagreements on the grading need to be given to me in writing. Disagreements will only be considered up to a week after the graded material is returned.
Classroom and campus safety are of paramount importance at Washington State University, and are the shared responsibility of the entire campus population. WSU urges students to follow the “Alert, Assess, Act” protocol for all types of emergencies and the “Run, Hide, Fight” response for an active shooter incident. Remain ALERT (through direct observation or emergency notification), ASSESS your specific situation, andACT in the most appropriate way to assure your own safety (and the safety of others if you are able).
Schedule of Classes: Note: Schedule may be adjusted slightly as the class progresses!
|Week||Date||Topic||Reading [One Article Per Week +]||Assignment|
|Jan 17||1. Introduction||-Ch2,Wikipedia,
|Jan 19||2. Matlab Tutorial||-Ch2||HW1 Out|
|Jan 24||3. Binary Image Processing||-Ch3; -Ch10|
|Jan 26||4. Morphology||-Ch3; -Ch9||HW2 Out|
|Wk 3||Jan 31||5. Image Acquisition||-Ch2||HW1 Due|
|Feb 02||6. Image calibration and Registration||-Ch5||HW3 Out|
|Wk 4||Feb 07||7. Image Enhancement||-Ch5; -Ch3
|HW2 Due: Formal report|
|Feb 09||8. Spatial Filtering||-Ch5; -Ch3||HW4 Out|
|Wk 5||Feb 14||9. Edge detection||-Ch5; -Ch4||HW3 Due|
|Feb 16||10. FFT and Frequency Domain Filtering||-Ch5; -Ch10||HW5 Out|
|Wk 6||Feb 21||11. Color and Color Image Processing||-Ch6; -Ch6||HW4 Due: Formal report|
|Feb 23||12. Multi/Hyper Spectral Image Analysis|
|Wk 7||Feb 28||13. Texture and Shape Analysis||-Ch7 ; -Ch11||HW5 Due|
|Mar 02||14. Segmentation||-Ch10; -Ch10||HW6 Out|
|Wk 8||Mar 07||15. Hough Transform||Project Proposal Due|
|Mar 09||16. Classification and Recognition 1||-Ch4; -Ch12||HW7 Out|
|Wk 9||Mar 14||17. Classification and Recognition 2||-Ch4||HW6 Due: Formal report|
|Mar 16||18. Soft Computing Techniques:
|Mar 19-23||Classes Recessed: Spring Vacation|
|Wk 10||Mar 28||19. Motion/Video Processing||-Ch 9; -Ch1||HW7 Due|
|Mar 30||20. Tracking – Kalman Filtering|
|Wk 11||Apr 04||21. 3D Vision Techniques and Sensors||-Ch12 – Ch13||Project Time|
|Apr 06||22. Stereo Vision||-Ch12 –Ch13|
|Wk 12||Apr 11||23. 3D Transformation||-Ch13|
|Apr 13||24. 3D Reconstruction 1||-Ch13|
|Wk 13||Apr 18||25. 3D Reconstruction 2||-Ch13||Project Time|
|Apr 20||26. Fuzzy Logic||-Ch4|
|Wk 14||Apr 25||27. Fuzzy Logic/Genetic Algorithm||Project Time|
|Apr 27||28. Genetic Algorithm|
|Wk 15||May 02||Project Time|
|May 04||Project Time|
|Wk 16||May 09||Project Presentation|
|May 11||Project Report Due|
*  L. Shapiro and G. Stockman, Computer Vision. ** Gonzalez et al. DIP with Matlab.