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Computer Vision Methods

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.


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 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 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 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 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 RANSAC RANSAC. NOTE: First lecture that will be streamed online. Please stay tuned for further info.
6 23.3.JM Retrieval, Minhash Image Retrieval for large image collections: image description, indexing, geometric consistency.
7 30.3.JCDeep learning A shallow introduction into the deep machine learning.
8 6.4.JCDeep learning II Deep learning for object detection. Further insights into the deep nets.
9 13.4. Easter Monday
10 20.4.JM Tracking I-III Mean Shift Tracking I. Introduction. Mean Shift
11 27.4.JM Tracking I-III KLT, KCF Tracking Tracking II. KLT tracker, KCF Kernel Correlation Filter.
12 4.5.JMTLD, Tracking_by_Segmentation, Dense_Correspondences Long-term Tracking, TLD: Tracking-Learning-Detection, Tracking by Segmentation, Dense Motion - Optical Flow, Scene Flow
13 11.5. JMViola-Jones face detector, Waldboost Object detection by sliding window and sequential decision making (Method of Viola and Jones, Waldboost)
14 18.5. JM 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

Plant recognition | Case study: Plant recognition using Deep Nets |


Work during the semester 50%, written part of the exam 40%, oral part of the exam 10%


Examples of exam 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.


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 forum of the course.

Good luck to all participants of the course.

jm_ct2008.11-3.jpg jancech.jpeg ondrej_drbohlav.jpg sulc.jpg
Jiří Matas Jan Čech Ondřej Drbohlav Milan Šulc

Consultations are possible upon request.

courses/mpv/start.txt · Last modified: 2020/04/17 14:27 by cechj