====== Computer Vision Methods ====== [[https://www.fel.cvut.cz/cz/education/rozvrhy-ng.B192/public/html/predmety/46/84/p4684506.html|Schedule on FEL (CZ course)]] [[https://www.fel.cvut.cz/cz/education/rozvrhy-ng.B192/public/html/predmety/46/85/p4685206.html|Schedule on FEL (EN course)]]\\ [[https://cw.felk.cvut.cz/upload/|Upload system]] [[https://cw.felk.cvut.cz/forum/forum-1639.html|Discussion forum]] [[https://cw.fel.cvut.cz/wiki/courses/mpv/labs/start|Labs]] ===== Course Description ===== This course is devoted to computer vision problems: Finding of correspondences between images using image features and their robust invariant descriptors, features matching, picture stitching, object and segment recognition in pictures or video, image retrieval and object tracking in the video sequences. ===== Pre-requisites ===== The computer vision methods course expects programming skills in Python and numpy computing environment. The programing assignments solving various computer vision methods are a substantial part of the [[https://cw.fel.cvut.cz/wiki/courses/mpv/labs/start|labs]]. The attendee is expected to know basics of digital image processing as convolution, filtration, intensity transformations, image function interpolations and basic geometric transformations of the image (see the first lab). The attended is also expected to govern basics of linear algebra and probability theory. ===== Lectures: Monday 11:00-12:30, KN:E-126 ===== Lecturers: JM Jiří Matas, JC Jan Čech, OD Ondřej Drbohlav, MS Milan Šulc ^week ^ Date ^ Lecturer ^ Slides ^ Topic ^ | 1| 17.2.|JM| {{ :courses:mpv:2019.02.18_local_features.pdf|Correspondence, 1st part}} | Interest point and distinguished regions detection: Harris operator (corner detection), Laplace operator and its approximation by difference of Gaussians, affine covariant version, Maximally Stable Extremal Regions (MSER).| | 2| 24.2.|JM| {{:courses:mpv:2019.02.18_local_features.pdf|Correspondence, 2nd part}} | Descriptors of measurement regions: SIFT (scale invariant feature transform), shape context. Local affine frames for geometric and photometric invariance of description.| | 3| 2.3.|JM| University closed for one day due to Coronavirus | | | 4| 9.3.|JM| {{2018_local-features-orig_new_jc.pdf|Correspondence, 3rd part}} | SIFT variants, LBP (local binary patterns), Finding correspondences and object recognition using local invariant description. | | | | | University closed due to Coronavirus from March 10 | | | | | | From March 16, lectures will be given using online streaming | | 5| 16.3.|JM| {{ :courses:mpv:2019_ransac.pdf |RANSAC}} | RANSAC. NOTE: First lecture that will be streamed online. Please stay tuned for further info.| | 6| 23.3.|JM| {{ :courses:mpv:2019.03.18-retrieval.pdf |Retrieval}}, {{ :courses:mpv:2010.03.15_minhash.pdf |Minhash}} |Image Retrieval for large image collections: image description, indexing, geometric consistency.| | 7| 30.3.|JC|{{deep_learning_MPV_2020.pdf|Deep learning}} |A shallow introduction into the deep machine learning.| | 8| 6.4.|JC|{{deep_learning_2_MPV_2020.pdf|Deep learning II}} | Deep learning for object detection. Further insights into the deep nets. | | 9| 13.4.| ^ Easter Monday | | | 10| 20.4.|JM| {{:courses:mpv:2019_tracking.pdf|Tracking I-III}} {{2019_mpv_mean_shift_2.pdf|Mean Shift}}| Tracking I. Introduction. Mean Shift | | 11| 27.4.|JM| {{:courses:mpv:2019_tracking.pdf|Tracking I-III}} {{ :courses:mpv:matas-2018.04-klt-only.pdf |KLT}}, {{kcf_lecture2016.pdf|KCF Tracking}}| Tracking II. KLT tracker, KCF Kernel Correlation Filter. | | 12| 4.5.|JM|{{TLD.pdf|TLD}}, {{Tracking_by_Segmentation.pdf|Tracking_by_Segmentation}}, {{Dense_Correspondences.pdf|Dense_Correspondences}}| Long-term Tracking, TLD: Tracking-Learning-Detection, Tracking by Segmentation, Dense Motion - Optical Flow, Scene Flow | | 13| 11.5.| JM|{{2016_05_viola_jones.pdf|Viola-Jones face detector}}, {{2014.05.25-waldboost-vision-apps.pdf| Waldboost}} | Object detection by sliding window and sequential decision making (Method of Viola and Jones, Waldboost) | | 14| 18.5.| JM |{{2016.05_hough-transform.pdf|Hough Transform}} | Detection of geometric primitives (lines, circles, elipses, etc.). Hough transform and its comparison with RANSAC(Random Sample Consensus).| * update of course slide material {{2019_fine_grained_recognition.pdf|Plant recognition}} | Case study: Plant recognition using Deep Nets | ===== Evaluation ===== Work during the semester 50%, written part of the exam 40%, oral part of the exam 10% ===== Exam ===== Examples of exam [[courses:mpv:labs:exam_questions|questions]]. There will be 4-5 similar questions at the written part of the exam. The oral part of the questions takes place after the written part and will focused on discussion of your answers. ===== Literature ===== Lecture slides constitute the main source of study literature in this course. ===== Further Info ===== Further information is available in next sections of this page. We would appreciate your feedback on the contents and organization on the discussion [[https://cw.felk.cvut.cz/forum/forum-1639.html|forum]] of the course. ----- \\ Good luck to all participants of the course. | Lecturers |||| | [[http://cmp.felk.cvut.cz/~matas|{{http://cmp.felk.cvut.cz/~matas/images/jm/jm_ct2008.11-3.jpg?120}}]] | [[http://cmp.felk.cvut.cz/~cechj|{{http://cmp.felk.cvut.cz/~cechj/JanCech.jpeg?120}}]] | [[http://cmp.felk.cvut.cz/~drbohlav/|{{http://cmp.felk.cvut.cz/~drbohlav/ondrej_drbohlav.jpg?120}}]]| [[http://cmp.felk.cvut.cz/~sulcmila/|{{:courses:mpv:sulc.jpg?120}}]] | | Jiří Matas | Jan Čech | Ondřej Drbohlav | Milan Šulc | Consultations are possible upon request.