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Instructions
| BE4M33MPV |
Computer Vision Methods |
| Roles: | PO, PS, PV |
Extent of teaching: | 2P+2C |
| Department: | 13133 |
Language of teaching: | EN |
| Guarantors: | Matas J. |
Completion: | Z,ZK |
| Lecturers: | Čech J., Matas J., Mishkin D., Tolias G. |
Credits: | 6 |
| Tutors: | Drbohlav O., Matas J., Mishkin D., Neumann L., Šuma P. |
Semester: | L |
Web page:
https://cw.fel.cvut.cz/wiki/courses/mpv/start
Anotation:
The course covers selected computer vision problems: search for correspondences between images via interest point detection, description and matching, image stitching, detection, recognition and segmentation of objects in images and videos, image retrieval from large databases and tracking of objects in video sequences.
This course is also part of the inter-university programme prg.ai Minor. It pools the best of AI education in Prague to provide students with a deeper and broader insight into the field of artificial intelligence. More information is available at
https://prg.ai/minor.
Study targets:
The methods for image registration, retrieval and for object detection and tracking are explained. In the labs, the students implement selected methods and test performance on real-world problems.
Course outlines:
| 1. | | Introduction. Course map. Overview of covered problems and application areas. |
| 2. | | Detectors of interest points and distinguished regions. Harris interest point (corner) detector, Laplace detector and its fast approximation as Difference of Gaussians, maximally stable extremal regions (MSER).Descriptions of algorithms, analysis of their robustness to geometric and photometric transformations of the image. |
| 3. | | Descriptors of interest regions. The local reference frame method for geometrically invariant description. The SIFT (scale invariant feature transform) descriptor, local binary patterns (LBP). |
| 4. | | Detection of geometric primitives, Hough transfrom. RANSAC (Random Sample and Consensus). |
| 5. | | Segmentation I. Image as a Markov random field (MRF). Algorithms formulating segmentation as a min-cut problem in a graph. |
| 6. | | Segmentation II. Level set methods. |
| 7. | | Inpainting. Semi-automatic simple replacement of a content of an image region without any visible artifacts. |
| 8. | | Object detection by the "scanning window" method, the Viola-Jones approach. |
| 9. | | Using local invariant description for object recognition and correspondence search. |
| 10. | | Tracking I. KLT tracker, Harris and correlation. |
| 11. | | Tracking II. Mean-shift, condensation. |
| 12. | | Image Retrieval I. Image descriptors for large databases. |
| 13. | | Image Retrieval II: Search in large databases, idexation, geometric verification |
| 14. | | Reserve |
Exercises outline:
| 1. | - | 5. Image stitching. Given a set of images with some overlap, automatically find corresponding points and estimate the geometric transformation between images. Create a single panoramic image by adjusting intensities of individual images and by stitching them into a single frame. |
| 6. | - | 9. Segmentation and impainting. Implement a simple impainting method, i.e. a method allowing semi-automatic simple replacement of a content of an image region without any visible artifacts. |
| 7. | - | 12. Detection of a instance of a class of objects (faces, cars, etc.) using the scanning window approach (Viola-Jones type detector). |
| 13. | - | 14. Submission and review of reports. |
Literature:
| 1. | | M. Sonka, V. Hlavac, R. Boyle. Image Processing, Analysis and Machine Vision. Thomson 2007 |
| 2. | | D. A. Forsyth, J. Ponce. Computer Vision: A Modern Approach. Prentice Hall 2003 |
Requirements:
Knowledge of calculus and linear algebra.
Note:
Keywords:
image registration, segmentation, object detection, tracking
Subject is included into these academic programs:
| Page updated 16.4.2024 09:51:56, semester: Z,L/2023-4, Z/2024-5, Send comments about the content to the Administrators of the Academic Programs |
Proposal and Realization: I. Halaška (K336), J. Novák (K336) |