PowerPoint 프레젠테이션
Changjae Oh
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Computer Vision
– Course Overview –
Semester 1, 22/23
Course Overview
Unit 1: Early vision / Low-level vision
• Introduction / Camera / Restoration / Feature detection
Unit 2: Mid-level vision
• Fitting / Grouping / Calibration / Epipolar /Stereo matching
Unit 4: Deep learning for computer vision
• Introduction / Loss / Backpropagation / CNN / Deep learning with practice
Unit 3: Mid-/High-level vision
• Tracking / Recognition / Detection
Course Overview
Unit 1: Early vision / Low-level vision
• Introduction / Camera / Restoration / Feature detection
• Lab1: Setting up – image/video representation in Python
• CT1: Early vision / Low-level vision
Unit 2: Mid-level vision
• Fitting / Grouping / Calibration / Epipolar /Stereo matching
• Lab2: Restoration and features
• CT2: Mid-level vision
Unit 4: Deep learning for computer vision
• Introduction / Loss / Backpropagation / CNN / Deep learning with practice
• Coursework report submission (Deadline: 23:59, 21th December 2021, UK time)
• CT4: Deep learning for computer vision
Unit 3: Mid-/High-level vision
• Tracking / Recognition / Detection
• Lab3: Fitting and grouping
• Lab4: Tracking and detection + In-lab assessment
• CT3: Mid-/High-level vision
Course Details – Module Delivery
• Blended Teaching – How?
̶ Lectures
= 50% live lectures + 50% recorded lectures
Telecom_M_G1
1 19:20-20:05
2 20:10-20:55
3 16:35-17:20
4 17:25-18:10
BUPT Week 5 6 7 8 9 10 11 12 13 14 15 16 17
w/c 19-Sep 26-Sep 3-Oct 10-Oct 17-Oct 24-Oct 31-Oct 7-Nov 14-Nov 21-Nov 28-Nov 5-Dec 12-Dec
1 Live Rec Rec Rec Rec Rec Rec Rec Rec Rec Rec Rec
2 Live Rec Rec Rec Rec Rec Rec Rec Rec Rec Rec Rec
3 Rec Live Tut Live Live Tut Live Live Tut Live Live Tut
4 Rec Live OH Live Live OH Live Live OH Live Live OH
Class Tests
CT1 L1 CT2 L2 L3/CT3 L4 CT4
Topics Unit 1 Unit 2 Unit 3 Unit 4
Telecom_M_G2
1 16:35-17:20
2 17:25-18:10
3 19:20-20:05
4 20:10-20:55
Course Details – Module Delivery
• Blended Teaching – How?
̶ Recorded Lectures
• To deliver theoretical/technical details
̶ Live lectures
• Review the past content + Interactive sessions (Quizzes and Q&A)
Telecom_M_G1
1 19:20-20:05
2 20:10-20:55
3 16:35-17:20
4 17:25-18:10
BUPT Week 5 6 7 8 9 10 11 12 13 14 15 16 17
w/c 19-Sep 26-Sep 3-Oct 10-Oct 17-Oct 24-Oct 31-Oct 7-Nov 14-Nov 21-Nov 28-Nov 5-Dec 12-Dec
1 Live Rec Rec Rec Rec Rec Rec Rec Rec Rec Rec Rec
2 Live Rec Rec Rec Rec Rec Rec Rec Rec Rec Rec Rec
3 Rec Live Tut Live Live Tut Live Live Tut Live Live Tut
4 Rec Live OH Live Live OH Live Live OH Live Live OH
Class Tests
CT1 L1 CT2 L2 L3/CT3 L4 CT4
Topics Unit 1 Unit 2 Unit 3 Unit 4
Telecom_M_G2
1 16:35-17:20
2 17:25-18:10
3 19:20-20:05
4 20:10-20:55
Course Details – Recorded Lectures
• Recorded Lectures
̶ Recorded Lectures
• To deliver theoretical/technical details
• Students should take the recorded lecture before the next live session
Telecom_M_G1
1 19:20-20:05
2 20:10-20:55
3 16:35-17:20
4 17:25-18:10
BUPT Week 5 6 7 8 9 10 11 12 13 14 15 16 17
w/c 19-Sep 26-Sep 3-Oct 10-Oct 17-Oct 24-Oct 31-Oct 7-Nov 14-Nov 21-Nov 28-Nov 5-Dec 12-Dec
1 Live Rec Rec Rec Rec Rec Rec Rec Rec Rec Rec Rec
2 Live Rec Rec Rec Rec Rec Rec Rec Rec Rec Rec Rec
3 Rec Live Tut Live Live Tut Live Live Tut Live Live Tut
4 Rec Live OH Live Live OH Live Live OH Live Live OH
Class Tests
CT2 L1 CT2 L2 L3/CT3 L4 CT4
Topics Unit 1 Unit 2 Unit 3 Unit 4
Telecom_M_G2
1 16:35-17:20
2 17:25-18:10
3 19:20-20:05
4 20:10-20:55
Course Details – Live Lectures
• Live lectures
̶ Brief review about past recorded lectures
̶ Interactive sessions using Mentimeter
• Going through exercises together + Q&A
Telecom_M_G1
1 19:20-20:05
2 20:10-20:55
3 16:35-17:20
4 17:25-18:10
BUPT Week 5 6 7 8 9 10 11 12 13 14 15 16 17
w/c 19-Sep 26-Sep 3-Oct 10-Oct 17-Oct 24-Oct 31-Oct 7-Nov 14-Nov 21-Nov 28-Nov 5-Dec 12-Dec
1 Live Rec Rec Rec Rec Rec Rec Rec Rec Rec Rec Rec
2 Live Rec Rec Rec Rec Rec Rec Rec Rec Rec Rec Rec
3 Rec Live Tut Live Live Tut Live Live Tut Live Live Tut
4 Rec Live OH Live Live OH Live Live OH Live Live OH
Class Tests
CT1 L1 CT2 L2 L3/CT3 L4 CT4
Topics Unit 1 Unit 2 Unit 3 Unit 4
Telecom_M_G2
1 16:35-17:20
2 17:25-18:10
3 19:20-20:05
4 20:10-20:55
• 4 times: 10th, 12th, 14th, 16th BUPT week
̶ Telecom_M_Y4_G1, Class 1-3: Monday 6,7 (13:00-14:35) (Room 4-138)
̶ Telecom_M_Y4_G1, Class 4-5: Monday 6,7 (13:00-14:35) (Room 1-101)
̶ Telecom_M_Y4_G2, Class 6-7: Thursday 3,4 (09:50-11:25) (Room 1-101)
̶ Telecom_M_Y4_G2, Class 8-10: Thursday 3,4 (09:50-11:25) (Room 4-138)
Telecom_M_G1
1 19:20-20:05
2 20:10-20:55
3 16:35-17:20
4 17:25-18:10
BUPT Week 5 6 7 8 9 10 11 12 13 14 15 16 17
w/c 19-Sep 26-Sep 3-Oct 10-Oct 17-Oct 24-Oct 31-Oct 7-Nov 14-Nov 21-Nov 28-Nov 5-Dec 12-Dec
1 Live Rec Rec Rec Rec Rec Rec Rec Rec Rec Rec Rec
2 Live Rec Rec Rec Rec Rec Rec Rec Rec Rec Rec Rec
3 Rec Live Tut Live Live Tut Live Live Tut Live Live Tut
4 Rec Live OH Live Live OH Live Live OH Live Live OH
Class Tests
CT1 L1 CT2 L2 L3/CT3 L4 CT4
Topics Unit 1 Unit 2 Unit 3 Unit 4
Telecom_M_G2
1 16:35-17:20
2 17:25-18:10
3 19:20-20:05
4 20:10-20:55
Assessment
• Exam (80%)
̶ One written exam
• Coursework (20%)
̶ Individual coursework (15%)
• Development of computer vision tasks
• Python and OpenCV
̶ In-class tests (5%)
• Test to be done in each office hour (Four in-class tests)
• Each test covers each unit’s content
• Easy questions using QMPlus
• Top 2 marks (out of 4 tests) will be counted. (2.5% each)
• Absence will be marked as zero (NO excuse of your absence will be accepted)
Assessment – Individual Coursework (1/3)
• In-lab assessment (30% of individual coursework)
̶ During the Lab4 hours
̶ Assessment of your coursework covered in Lab1-3
• Report (70% of individual coursework)
̶ use the provided layout, with provided guideline
̶ at the end of the semester (Deadline: 23:59, 21st December 2022 (UK time))
̶ One .py code to each problem
• Zero mark will be given if the result is not reproducible
• Zero mark will be given if any unauthorized library is used
Assessment – Individual Coursework (2/3)
• In-lab assessment (30%)
̶ will be evaluated by
1) running the implemented codes,
2) checking during the in-lab assessment: the understanding of the tasks with a short
conversation with a TA
• Report (70%)
̶ will be evaluated based on
1) the quality of the analysis
2) the discussion of the results obtained in the coursework tasks
Assessment – Individual Coursework (3/3)
̶ A dataset provided from this module (image + video)
→ Quantitative assessment
̶ A dataset collected by yourself (image + video)
→ Qualitative assessment
Assessment – Coursework Submission@ QMplus
• Submit 1) your report and 2) zip file to the QMplus.
̶ QMplus submission example:
• EBU7240_CHANGJAE_OH_19XXXXXXX.pdf
• EBU7240_CHANGJAE_OH_19XXXXXXX.zip
̶ The zip file will contain the following folders:
̶ Name the zip file you submit as:
̶ Max size of the zip file: 50M
̶ The outputs of your implementations should be generated in the \results directory
• No need to submit the outputs of your code (we will reproduce them!), just make the
\results directory
EBU7240_FIRSTNAME_FAMILYNAME_QMSTUDNETNUMBER
├── inputs
├── results
23:59, 21th Dec 2022
In-class Test
• Four in-class tests
̶ To be done in each office hour
• Less than 10 min
• Students should be in the classroom (Scores will be accepted ONLY WHEN attendance is recorded)
̶ Easy online-test using QMPlus
• Each test covers each unit’s content
• Questions for Telecom_M_G1 and Telecom_M_G2 will be different
• For a fairness issues, ONLY the problems used in the live-session will be covered (with modification)
̶ Top 2 marks will be counted. (2.5% each)
• Absence will be marked as zero
• Any excuse of your absence will NOT be accepted
Office Hours
̶ During office hours (OH), but after the class test (<10 min) ̶ MS Teams • I will post the meeting link through QMPlus • Anyone can drop in with his/her own MS Teams account and have a video meeting A few tips – Exam • Define, define, define! ̶ ex) EBU6230- Image Video processing Opening: M·S=(M S)⊕S A few tips – Coursework • There are several traps to prevent your plagiarism ̶ Don’t copy others ̶ You’ll (sometimes) need to create your own dataset By the end of this module, you will • understand fundamental tasks involved in computer vision tasks • understand the principle of deep learning in computer vision • become familiar with ̶ the various important techniques in computer vision ̶ Python and OpenCV 程序代写 CS代考 加微信: powcoder QQ: 1823890830 Email: powcoder@163.com