[[https://www.fel.cvut.cz/cz/education/rozvrhy-ng.B162/public/html/predmety/46/84/p4684506.html|Schedule]] [[https://www.fel.cvut.cz/cz/education/rozvrhy-ng.B162/public/cz/predmety/46/84/fsl-p4684506.html|Students of the course]] [[https://cw.felk.cvut.cz/upload/|Upload system]] [[https://cw.felk.cvut.cz/forum/forum-1387.html|Discussion forum]] ====== Computer Vision Methods Labs ====== ===== Labs plan ===== Labs are organized in four main topics: Correspondence problem, Indexing and image retrieval, Object tracking, Convolutional neural networks. Each topic is covered by approximately two to four labs. \\ ^ Lab ^ Date ^ Topic ^ | 1 | 21.2. | [[courses:ae4m33mpv:labs:1_intro:start|Introduction to Image Processing in MATLAB]]. | | 2 | 28.3. | [[courses:ae4m33mpv:labs:2_correspondence_problem:start| Correspondence problem I, detection of the interest points]]. | | 3 | 7.3. | [[courses:ae4m33mpv:labs:2_correspondence_problem:start#computing_local_invariant_description| Correspondence problem II, computing local invariant description]]. | | 4 | 14.3. | [[courses:ae4m33mpv:labs:2_correspondence_problem:start#correspondence_problem_and_ransac| Correspondence problem III, finding tenative correspondences and RANSAC]]. | | 5 | 21.3. | [[courses:ae4m33mpv:labs:2_correspondence_problem:start| Correspondence problem, summary]]. | | 6 | 28.3. | [[courses:ae4m33mpv:labs:3_indexing:start#image_representation_with_set_of_visual_words_tf-idf_weighting|Indexing I, image representation with set of visual words. TF-IDF weighting]] | | 7 | 4.4. | [[courses:ae4m33mpv:labs:3_indexing:start#fast_spatial_verification_query_expansion|Indexing II, fast spatial verification, query expansion.]] | | 8 | 11.4. | [[courses:ae4m33mpv:labs:3_indexing:start#your_taskimage_retrieval_in_big_databases|Indexing III, complete pipeline for image retrieval in big databases.]] | | 9 | 18.4. | [[courses:ae4m33mpv:labs:5_convolutional_networks:start|Convolutional Neural Networks I.]] | | 10 | 25.4. | [[courses:ae4m33mpv:labs:5_convolutional_networks:start#training_own_network| Convolutional Neural Networks II.]] | | 11 | 2.5. | [[courses:ae4m33mpv:labs:4_tracking:start|Tracking I, Kanade-Lucas-Tomasi tracking (KLT tracker)]]. | | 12 | 9.5. | [[courses:ae4m33mpv:labs:4_tracking:4b_tracking_kcf|Tracking II, Correlation tracking (KCF tracker)]]. | | 13 | 16.5. | "Rektorský den" (A day without teaching at the CTU). | | 14 | 23.5. | Consultations on exam questions. | Labs will be accompanied with a simple programming task. Detailed specification of the tasks is described in each of the labs. Students will upload their results and their codes through the [[https://cw.felk.cvut.cz/upload/|BRUTE]] system. Each lab will usually consists of three parts: - **Discussion on the last lecture**. Students will be free to ask any questions related to the last lecture. At the end of this session, a teacher will pose a question to a volunteer/random student. If the student answers correctly, he/she will get a single //bonus point//. - **Working on the current task**. Students are free to ask any specific questions, discuss their current results, resolve any programming issues. - **Short introduction of the next task**. The teacher will briefly introduce the next problem, give some hints and answer possible questions. /* Each lab will be accompanied with a programming task. The programming tasks are due to the next lab (midnight of the Tuesday before the lab) and should be solved regularly through the semester and uploaded to the upload system. Detailed specifications of the tasks are described in each of the labs. Please, read the description of the task **before** the lab. The upload system for uploading and checking your assignements is available [[https://cw.felk.cvut.cz/upload/|here]].\\ */ ===== Assessment ===== You are obliged to carry out all programming tasks at least a minimal required quality. All tasks must be carried out individually! You are free to discuss the problems with your colleagues, however the code must be written strictly by yourself. See [[https://cw.fel.cvut.cz/wiki/help/common/plagiarism_cheating|plagiarism]] if you are unsure what is allowed. Your participation on the labs is not mandatory, however we will not provide any extra consultations besides those during the labs. ===== Evaluation Policy ===== The grading points from the labs will contribute to 50 percent of your course evaluation. It is required to obtain at least half of the points for each programming task. Each task uploaded after the due date is penalized as follows:\\ Upload late by 6 - 24 hours => -10% of the points, 24 - 48 hours => -25% of the points and finally 48 - 72 hours => 40% of the maximum points. Tasks not delivered until 72 hours after the due date (i.e. three days after the due date) without a relevant excuse will not be accepted and automatically lead to failing the course.\\ ===== Useful Links ===== [[https://www.fel.cvut.cz/cz/education/rozvrhy-ng.B162/public/cz/predmety/46/84/p4684506.html|Schedule]]\\ [[https://www.fel.cvut.cz/cz/education/rozvrhy-ng.B162/public/cz/paralelky/C46/84/par4684506.101.html|Students of the course]]\\ [[http://vision.stanford.edu/teaching/cs223b/video.html|Computer vision: Facts & Fiction series]]\\ [[http://www.andrew.cmu.edu/course/16-720/|Computer Vision course at CMU]] | Course Assistants |||| | {{:courses:ae4m33mpv:labs:javier_aldana_2013.jpg?100|}} | [[http://cmp.felk.cvut.cz/~cechj|{{http://cmp.felk.cvut.cz/~cechj/JanCech.jpeg?90x120}}]] | [[http://cmp.felk.cvut.cz/~sulcmila/|{{http://cmp.felk.cvut.cz/~sulcmila/img/web_photo.jpg?90}}]] | [[http://cmp.felk.cvut.cz/~drbohlav/|{{http://cmp.felk.cvut.cz/~drbohlav/ondrej_drbohlav.jpg?90x120}}]] | | Javier Aldana | Jan Čech \\ (lead) | Milan Šulc | Ondřej Drbohlav |