Course : MRES.B.02.01 Selected Topics in image Processing and Computer Vision

Course code : REEE108

Course Description

This is the image of course

Computer vision is perhaps one of the most thrilling fields which combines the concepts of data-driven Machine Learning and image processing. Computer vision exists in numerous applications ranging from Navigation, e.g., by any type of an autonomous vehicle; document analysis and understanding, mixed reality etc. The course module contains selected topics in computer vision and pattern recognition.

  • Course Objectives/Goals

    Upon successful completion of the course, students are expected to be able to:

    1. Identify basic concepts, terminology, theories, models and methods in the field of digital image processing and computer vision. 
    2. Describe basic methods of computer vision related to multi-scale representation, edge detection
      and detection of other primitives,for object recognition. 
    3. Suggest a design of a computer vision system for a specific problem. 
    4. Describe known principles of human visual system by means of the Bag of visual Words model and Sparse Representation principles. 

    Prerequisites/Prior Knowledge

    Undergraduate courses dedication on Digital signal and image processing, Linear algebra, probability and statistics, and Pattern recognition would be useful.

    Furthermore, the potential students should have a minimal knowledge of MATLAB and/or Python. 

    Assessment Methods

    Student evaluation comes from: 

    a) One mini-project regarding the use of typical image processing methods for object recognition.

    b) One mini-project regarding the use of a bag-of-words model for object rcognition. 

    Bibliography

    TextBooks

    1. Computer Vision - A modern approach, by D. Forsyth and J. Ponce, Prentice Hall Robot Vision, by B. K. P. Horn, McGraw-Hill, ISBN 978-0136085928.

    2. Digital Image Processing 4th Edition by Rafael Gonzalez, Richard Woods, Pearson, ISBN: 9780133356724. 

     

    Reference Books

    1. Richard Szeliksy “Computer Vision: Algorithms and Applications” (http://szeliski.org/Book/).

    2. Multiple View Geometry in Computer Vision Second Edition, Richard Hartley and Andrew Zisserman, Cambridge University Press, March 2004 (https://www.robots.ox.ac.uk/~vgg/hzbook/).

    3. Sparse and Redundant Representations: From Theory to Applications in Signal and Image Processing 2010th Edition by Michael Elad, (https://elad.cs.technion.ac.il/publications/other/).

    4. Machine Learning A Bayesian and Optimization Perspective 2nd Edition, Sergios Theodoridis, ISBN: 9780128188033. 

     

    Additional info

    RESEARCH ARTICLES

    1. An anthology of research papers offered by: 

    A. Computer Vision Foundation. openaccess.thecvf.com/menu. Research papers from top notch conferences such as: 

    • Computer Vision & Pattern Recognition (CVPR)
    • International Conference on Computer Vision (ICCV)
    • Winter Applications on Computer Vision (WACV)

    B. European Computer Vision Association repository, www.ecva.net/papers.php. Research papers from top notch conferences such as:

    • European Conference on Computer Vision (ECCV)

    C.  IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI) 

    D. IEEE signal processing society

     

    2. Highly cited resarch papers.

    • Aharon, M., Elad, M. and Bruckstein, A., 2006. K-SVD: An algorithm for designing overcomplete dictionaries for sparse representation. IEEE Transactions on signal processing54(11), pp.4311-4322.
    • Wright, J., Ma, Y., Mairal, J., Sapiro, G., Huang, T.S. and Yan, S., 2010. Sparse representation for computer vision and pattern recognition. Proceedings of the IEEE98(6), pp.1031-1044.
    • LeCun, Yann, Yoshua Bengio, and Geoffrey Hinton. "Deep learning." nature 521.7553 (2015): 436-444.
    • LeCun, Y., Bottou, L., Bengio, Y. and Haffner, P., 1998. Gradient-based learning applied to document recognition. Proceedings of the IEEE86(11), pp.2278-2324.
    • Fei-Fei, Li, Robert Fergus, and Pietro Perona. "One-shot learning of object categories." IEEE transactions on pattern analysis and machine intelligence 28, no. 4 (2006): 594-611.
    • Lowe, David G. "Distinctive image features from scale-invariant keypoints." International journal of computer vision 60 (2004): 91-110.
    • Perona, Pietro, and Jitendra Malik. "Scale-space and edge detection using anisotropic diffusion." IEEE Transactions on pattern analysis and machine intelligence 12, no. 7 (1990): 629-639.
    • Simonyan, Karen, and Andrew Zisserman. "Very deep convolutional networks for large-scale image recognition." arXiv preprint arXiv:1409.1556 (2014).
    • Olshausen, Bruno A., and David J. Field. "Emergence of simple-cell receptive field properties by learning a sparse code for natural images." Nature 381, no. 6583 (1996): 607-609.
    • Ng, Andrew, Michael Jordan, and Yair Weiss. "On spectral clustering: Analysis and an algorithm." Advances in neural information processing systems 14 (2001).
    • He, Kaiming, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. "Deep residual learning for image recognition." In Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 770-778. 2016.
    • Tuzel, O., Porikli, F., & Meer, P. (2008). Pedestrian detection via classification on riemannian manifolds. IEEE transactions on pattern analysis and machine intelligence30(10), 1713-1727.
    • Deng, Jia, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei. "Imagenet: A large-scale hierarchical image database." In 2009 IEEE conference on computer vision and pattern recognition, pp. 248-255. Ieee, 2009.
    • Lazebnik, Svetlana, Cordelia Schmid, and Jean Ponce. "Beyond bags of features: Spatial pyramid matching for recognizing natural scene categories." In 2006 IEEE computer society conference on computer vision and pattern recognition (CVPR'06), vol. 2, pp. 2169-2178. IEEE, 2006.
    • Jiang, Xingyu, Jiayi Ma, Guobao Xiao, Zhenfeng Shao, and Xiaojie Guo. "A review of multimodal image matching: Methods and applications." Information Fusion 73 (2021): 22-71.
    • Mikolajczyk, Krystian, Tinne Tuytelaars, Cordelia Schmid, Andrew Zisserman, Jiri Matas, Frederik Schaffalitzky, Timor Kadir, and L. Van Gool. "A comparison of affine region detectors." International journal of computer vision 65 (2005): 43-72.

     

    TOOLS

    VLFEAT: https://www.vlfeat.org/

    SPAMS: SPArse Modeling Software 

    TENSORFLOW: www.tensorflow.org/

    OPENCV: opencv.org/

    PyTorch: pytorch.org/

    MANOPT: www.manopt.org/

     

    WEBSITES

    Google machine learning education: https://developers.google.com/machine-learning

    Prof. M. Harandi website: https://sites.google.com/site/mehrtashharandi/ 

    Prof. M. Elad website: https://elad.cs.technion.ac.il/

    Prof. F. Porikli website: https://www.porikli.com/

    Prof. A. Ng website: https://www.andrewng.org/

    Prof. Y. LeCun website: http://yann.lecun.com/

    Prof. A. Zisserman website: https://www.robots.ox.ac.uk/~az/

    Prof. Fei-Fei Li website: http://vision.stanford.edu/

    Stanford University DL-CV: cs231n.stanford.edu/index.html

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