25/10/2021 1 CSCI435/CSCI935 Computer Vision – Algorithms and Systems Subject Review & Final Exam Lecturer: Assoc/Prof Wanqing Li Room 3.101 Email: wanqing@uow.edu.au Web: http://www.uow.edu.au/~wanqing 25/10/2021 2 Subject Learning Outcomes On successful completion of this subject, students are expected to: • Understand the principle of digital image and video cameras. • Use image enhancement techniques. • Use object detection and recognition techniques. • Use video processing techniques to detect moving objects. • Design and implement basic computer vision systems for real applications. Topics Covered in the Subject  Photometry and colourimetry  light, colour perception and colour spaces  Image acquisition  Optical system. sampling, image sensors, single sensor based digital camera, colour processing chain  Image quality & enhancement  Criteria of quality, sharpness, low- & high-pass filter in spatial and frequency domain, enhancement, noise, image spectrum and pyramids  Edge detection  Gradient, edge detection operators, zero-crossing, LoG, DoG, Canny edge detector  Key point detection  Harris corner detection, SIFT interest points and descriptors, BoW, image similarity 25/10/2021 3 Topics Covered in the Subject  Shape detection  Hough transform (line), circle detection  Image segmentation  Visual features, perceptual grouping, thresholding (heuristic & Otsu’s), clustering-based (k-means, mean-shift)  Binary image processing  Binary morphology, connected component analysis  CD and background modelling  Robust CD, Background modelling (running average/median/Gaussian GMM)  Object detection  General framework (detection as classification), sliding window vs. reginal proposal (selective search), skin-colour based face detection, AdaBoost (Viola & Jones detector), HoG for detection of humen and faces 25/10/2021 4 Topics Covered in the Subject  Image classification and object recognition  General framework, human perception of faces, face recognition system, normalization of faces, eigenfaces, LBP-based face recognition  Motion estimation  Optical flow, HS method, LK method, global motion, motion analysis and its applications  Convolitional Neural Networks (ConvNets)  Linear classifier, softmax classifiers, optimization, multiple layer perceptron (fully connected layers), gradient backpropagation, convolutional layers, learning ConvNet parameters (mini-batch SGD, batch normalization), hyper-parameters, regularization and dropout, data augmentation, typical ConvNets for CV 25/10/2021 5 25/10/2021 6 Subject Materials for Review Lecture slides:  Available on the subject Moodle. Recommended books:  D. Forsyth, J. Ponce. Computer Vision a Modern Approach, Prentice Hall, 2012 (2nd ed.)  E. R Davies, Computer and machine vision: theory, algorithms and practicalities, Academic Press; 4th edition; 2012  Stanford's course Convolutional Neural Networks for Visual Recognition http://cs231n.stanford.edu/ Assignments Assessments Assignments (60% in total)  3x Coding projects 3 = 60% Final Exam (40%)  Minimum requirement 40% = 16 marks 25/10/2021 7 Final Examination Materials and Aids Allowed  Open book Exam Structure  Problem solving and discussion  4 questions, 10% each  Each question has multiple sub-questions This exam will run via Moodle 25/10/2021 8 25/10/2021 9 Final Examination… Exam Date & Starting time  13:30 (Sydney time) Monday 15 November 2021  Please check SOLS Exam Duration  2 hours Grace Period • 30 minutes for preparing and submitting answer sheets in a single pdf file Final Examination - Instructions Have a set of A4 blank paper ready On the first page, write  Your full name, Student Number & UOW login name Answer each question on a separate page clearly  either handwriting or using suitable editing software at your own choice Scan or take photos of your answer sheets and convert them into one single pdf file (<200MB) Name the pdf file as  

25/10/2021 1 CSCI435/CSCI935 Computer Vision – Algorithms and Systems Subject Review & Final Exam Lecturer: Assoc/Prof Wanqing Li Room 3.101 Email: wanqing@uow.edu.au Web: http://www.uow.edu.au/~wanqing 25/10/2021 2 Subject Learning Outcomes On successful completion of this subject, students are expected to: • Understand the principle of digital image and video cameras. • Use image enhancement techniques. • Use object detection and recognition techniques. • Use video processing techniques to detect moving objects. • Design and implement basic computer vision systems for real applications. Topics Covered in the Subject  Photometry and colourimetry  light, colour perception and colour spaces  Image acquisition  Optical system. sampling, image sensors, single sensor based digital camera, colour processing chain  Image quality & enhancement  Criteria of quality, sharpness, low- & high-pass filter in spatial and frequency domain, enhancement, noise, image spectrum and pyramids  Edge detection  Gradient, edge detection operators, zero-crossing, LoG, DoG, Canny edge detector  Key point detection  Harris corner detection, SIFT interest points and descriptors, BoW, image similarity 25/10/2021 3 Topics Covered in the Subject  Shape detection  Hough transform (line), circle detection  Image segmentation  Visual features, perceptual grouping, thresholding (heuristic & Otsu’s), clustering-based (k-means, mean-shift)  Binary image processing  Binary morphology, connected component analysis  CD and background modelling  Robust CD, Background modelling (running average/median/Gaussian GMM)  Object detection  General framework (detection as classification), sliding window vs. reginal proposal (selective search), skin-colour based face detection, AdaBoost (Viola & Jones detector), HoG for detection of humen and faces 25/10/2021 4 Topics Covered in the Subject  Image classification and object recognition  General framework, human perception of faces, face recognition system, normalization of faces, eigenfaces, LBP-based face recognition  Motion estimation  Optical flow, HS method, LK method, global motion, motion analysis and its applications  Convolitional Neural Networks (ConvNets)  Linear classifier, softmax classifiers, optimization, multiple layer perceptron (fully connected layers), gradient backpropagation, convolutional layers, learning ConvNet parameters (mini-batch SGD, batch normalization), hyper-parameters, regularization and dropout, data augmentation, typical ConvNets for CV 25/10/2021 5 25/10/2021 6 Subject Materials for Review Lecture slides:  Available on the subject Moodle. Recommended books:  D. Forsyth, J. Ponce. Computer Vision a Modern Approach, Prentice Hall, 2012 (2nd ed.)  E. R Davies, Computer and machine vision: theory, algorithms and practicalities, Academic Press; 4th edition; 2012  Stanford's course Convolutional Neural Networks for Visual Recognition http://cs231n.stanford.edu/ Assignments Assessments Assignments (60% in total)  3x Coding projects 3 = 60% Final Exam (40%)  Minimum requirement 40% = 16 marks 25/10/2021 7 Final Examination Materials and Aids Allowed  Open book Exam Structure  Problem solving and discussion  4 questions, 10% each  Each question has multiple sub-questions This exam will run via Moodle 25/10/2021 8 25/10/2021 9 Final Examination… Exam Date & Starting time  13:30 (Sydney time) Monday 15 November 2021  Please check SOLS Exam Duration  2 hours Grace Period • 30 minutes for preparing and submitting answer sheets in a single pdf file Final Examination - Instructions Have a set of A4 blank paper ready On the first page, write  Your full name, Student Number & UOW login name Answer each question on a separate page clearly  either handwriting or using suitable editing software at your own choice Scan or take photos of your answer sheets and convert them into one single pdf file (<200MB) Name the pdf file as 