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This is the curriculum for "Learn Computer Vision" by Siraj Raval on Youtube
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| 2026-07-31 | 1122 |
| 2026-08-04 | 1122 |
| 2026-08-06 | 1122 |
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# Learn_Computer_Vision This is the curriculum for "Learn Computer Vision" by Siraj Raval on Youtube ## Course Objective This is the Curriculum for [this](https://youtu.be/FSe_02FpJas) video on Learn Computer Vision by Siraj Raval on Youtube. After completing this course, start your own startup, do consulting work, or find a full-time job related to Computer Vision. Remember to believe in your ability to learn. You can learn CV , you will learn CV, and if you stick to it, eventually you will master it. ## Find a study buddy Join the #Computer_Vision_curriculum channel in our Slack channel to find one http://wizards.herokuapp.com ## Components each week - Video Lectures - Reading Assignments - Project(s) ## Course Length - 8 weeks - 2-3 Hours of Study per Day ## Tools Used - Python, OpenCV, Tensorflow ## Prerequisites - Learn Python https://www.edx.org/course/introduction-to-python-for-data-science-3 - Calculus http://tutorial.math.lamar.edu/pdf/Calculus_Cheat_Sheet_All.pdf - Linear Algebra https://www.souravsengupta.com/cds2016/lectures/Savov_Notes.pdf ## Part 1: Low Level Vision (image > image) ### Week 1 ( Basic Image Processing Techniques) - Luminance (Brightness, contrast, gamma, histogram equalization) - Linear Filtering (enhance image - blur & sharpen, edge detect, image countours, convolution) - Non Linear Filtering (Median, Bilateral Filter, morphology ) - Color processing (B&W, Saturation, White Balance) - Dithering (Quantization, Ordered Dither, Floyd-Steinberg) - Blending (Image pyramids) - Texture Analysis - Template Matching (find object in an image) #### Video Lectures - https://www.youtube.com/watch?v=-nt80JUNwlw&list=PLjMXczUzEYcHvw5YYSU92WrY8IwhTuq7p&index=2 videos 1-5 #### Reading Assignments - http://szeliski.org/Book/drafts/SzeliskiBook_20100903_draft.pdf Sec 3.1.1-2, 3.2 Sec 3.2.3, 4.2 3.3.2-4 #### Project - Detect an object in an image via the OpenCV Library ### Week 2 (Motion and Optical Flow) - Motion Analysis - Optical Flow #### Video Lectures - https://www.udacity.com/course/introduction-to-computer-vision--ud810 Udacity lesson 6 - https://www.youtube.com/watch?v=-nt80JUNwlw&list=PLjMXczUzEYcHvw5YYSU92WrY8IwhTuq7p&index=2 video 8 - https://www.youtube.com/watch?v=wC8hXuHsHAQ&list=PLvqB6_mDBCdlnT84LK_NvbOqcXLlOTR8j&index=6&t=0s #### Reading Assignments - http://szeliski.org/Book/drafts/SzeliskiBook_20100903_draft.pdf Sec 10.5 Sec 8.4 (up until 8.4.1) #### Project - Track a moving object in a video frame with OpenCV ### Part 2: Mid Level Vision (image > features) #### Week 3 (Basic Segmentation) - Segmentation and clustering algorithms like watershed, grabcut - Interactive segmentation - Hough transform (detect circles, lines) - Foreground Extraction #### Video Lectures - https://www.youtube.com/watch?v=ZF-3aORwEc0 - https://www.youtube.com/watch?v=3qJej6wgezA #### Reading Assignments - Sec Sec 5.2-5.4 http://szeliski.org/Book/drafts/SzeliskiBook_20100903_draft.pdf #### Project - Segment Lane lines in a road image with OpenCV #### Week 4 (Fitting) - Fitting lines and curves - Robust fitting, RANSAC - Deformable contours #### Video Lectures - Videos 6-7 https://www.youtube.com/watch?v=-nt80JUNwlw&list=PLjMXczUzEYcHvw5YYSU92WrY8IwhTuq7p&index=2 #### Reading Assignments - Sec 4.3.2 5.1.1 http://szeliski.org/Book/drafts/SzeliskiBook_20100903_draft.pdf #### Project - Compute Vanishing Points in a hallway image with OpenCV ### Part 3: Multiple Views #### Week 5 (Multiple Images) - Local invariant feature detection and description - Image transformations and alignment - Planar homography - Epipolar geometry and stereo - Object instance recognition #### Video Lectures - https://www.youtube.com/playlist?list=PLyH-5mHPFffFvCCZcbdWXAb_cTy4ZG3Dj #### Reading Assignments - http://vision.cs.utexas.edu/376-spring2018/#Tues_May_1 see the associated readings on this page #### Project - Turn a set of images into a 3D Object with OpenCV #### Week 6 (3D Scenes) - Stereo Vision, Dense Mot
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