{"id":13935316,"url":"https://github.com/llSourcell/Learn_Computer_Vision","last_synced_at":"2025-07-19T20:31:50.737Z","repository":{"id":47865636,"uuid":"196923798","full_name":"llSourcell/Learn_Computer_Vision","owner":"llSourcell","description":"This is the curriculum for \"Learn Computer Vision\" by Siraj Raval on Youtube","archived":false,"fork":false,"pushed_at":"2021-08-12T07:50:43.000Z","size":23,"stargazers_count":1104,"open_issues_count":7,"forks_count":353,"subscribers_count":69,"default_branch":"master","last_synced_at":"2024-11-27T02:35:59.518Z","etag":null,"topics":[],"latest_commit_sha":null,"homepage":null,"language":null,"has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":null,"status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/llSourcell.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":null,"code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null}},"created_at":"2019-07-15T04:27:45.000Z","updated_at":"2024-11-15T09:46:10.000Z","dependencies_parsed_at":"2022-09-14T09:51:53.189Z","dependency_job_id":null,"html_url":"https://github.com/llSourcell/Learn_Computer_Vision","commit_stats":null,"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/llSourcell/Learn_Computer_Vision","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/llSourcell%2FLearn_Computer_Vision","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/llSourcell%2FLearn_Computer_Vision/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/llSourcell%2FLearn_Computer_Vision/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/llSourcell%2FLearn_Computer_Vision/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/llSourcell","download_url":"https://codeload.github.com/llSourcell/Learn_Computer_Vision/tar.gz/refs/heads/master","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/llSourcell%2FLearn_Computer_Vision/sbom","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":266007439,"owners_count":23863529,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":[],"created_at":"2024-08-07T23:01:35.637Z","updated_at":"2025-07-19T20:31:50.725Z","avatar_url":"https://github.com/llSourcell.png","language":null,"readme":"# Learn_Computer_Vision\nThis is the curriculum for \"Learn Computer Vision\" by Siraj Raval on Youtube\n\n## Course Objective\nThis 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.\nRemember to believe in your ability to learn. You can learn CV , you will learn CV, and if you stick to it,\neventually you will master it.\n\n## Find a study buddy\nJoin the #Computer_Vision_curriculum channel in our Slack channel to find one http://wizards.herokuapp.com\n\n## Components each week\n- Video Lectures\n- Reading Assignments\n- Project(s) \n\n## Course Length\n- 8 weeks\n- 2-3 Hours of Study per Day\n\n## Tools Used\n- Python, OpenCV, Tensorflow \n\n## Prerequisites\n\n- Learn Python https://www.edx.org/course/introduction-to-python-for-data-science-3\n- Calculus http://tutorial.math.lamar.edu/pdf/Calculus_Cheat_Sheet_All.pdf \n- Linear Algebra https://www.souravsengupta.com/cds2016/lectures/Savov_Notes.pdf \n\n## Part 1: Low Level Vision (image \u003e image)\n\n### Week 1 ( Basic Image Processing Techniques)\n- Luminance (Brightness, contrast, gamma, histogram equalization)\n- Linear Filtering (enhance image - blur \u0026 sharpen, edge detect, image countours, convolution)\n- Non Linear Filtering (Median, Bilateral Filter, morphology )\n- Color processing (B\u0026W, Saturation, White Balance)\n- Dithering (Quantization, Ordered Dither, Floyd-Steinberg)\n- Blending (Image pyramids)\n- Texture Analysis\n- Template Matching (find object in an image)\n\n#### Video Lectures\n- https://www.youtube.com/watch?v=-nt80JUNwlw\u0026list=PLjMXczUzEYcHvw5YYSU92WrY8IwhTuq7p\u0026index=2 videos 1-5 \n#### Reading Assignments\n- 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\n#### Project\n- Detect an object in an image via the OpenCV Library\n\n### Week 2 (Motion and Optical Flow)\n- Motion Analysis\n- Optical Flow\n#### Video Lectures\n- https://www.udacity.com/course/introduction-to-computer-vision--ud810 Udacity lesson 6\n- https://www.youtube.com/watch?v=-nt80JUNwlw\u0026list=PLjMXczUzEYcHvw5YYSU92WrY8IwhTuq7p\u0026index=2 video 8\n- https://www.youtube.com/watch?v=wC8hXuHsHAQ\u0026list=PLvqB6_mDBCdlnT84LK_NvbOqcXLlOTR8j\u0026index=6\u0026t=0s \n#### Reading Assignments\n- http://szeliski.org/Book/drafts/SzeliskiBook_20100903_draft.pdf   Sec 10.5 Sec 8.4 (up until 8.4.1)\n#### Project\n- Track a moving object in a video frame with OpenCV\n\n### Part 2: Mid Level Vision (image \u003e features)\n\n#### Week 3 (Basic Segmentation)\n- Segmentation and clustering algorithms like watershed, grabcut\n- Interactive segmentation\n- Hough transform (detect circles, lines)\n- Foreground Extraction\n\n#### Video Lectures\n- https://www.youtube.com/watch?v=ZF-3aORwEc0\n- https://www.youtube.com/watch?v=3qJej6wgezA\n#### Reading Assignments\n- Sec Sec 5.2-5.4 http://szeliski.org/Book/drafts/SzeliskiBook_20100903_draft.pdf   \n#### Project\n- Segment Lane lines in a road image with OpenCV\n\n#### Week 4 (Fitting)\n- Fitting lines and curves\n- Robust fitting, RANSAC\n- Deformable contours\n#### Video Lectures\n- Videos 6-7 https://www.youtube.com/watch?v=-nt80JUNwlw\u0026list=PLjMXczUzEYcHvw5YYSU92WrY8IwhTuq7p\u0026index=2 \n#### Reading Assignments\n- Sec 4.3.2 5.1.1 http://szeliski.org/Book/drafts/SzeliskiBook_20100903_draft.pdf    \n#### Project\n- Compute Vanishing Points in a hallway image with OpenCV\n\n### Part 3: Multiple Views\n\n#### Week 5 (Multiple Images)\n- Local invariant feature detection and description\n- Image transformations and alignment\n- Planar homography\n- Epipolar geometry and stereo\n- Object instance recognition\n#### Video Lectures\n- https://www.youtube.com/playlist?list=PLyH-5mHPFffFvCCZcbdWXAb_cTy4ZG3Dj \n#### Reading Assignments\n- http://vision.cs.utexas.edu/376-spring2018/#Tues_May_1 see the associated readings on this page\n#### Project\n- Turn a set of images into a 3D Object with OpenCV\n\n#### Week 6 (3D Scenes)\n- Stereo Vision, Dense Motion and Tracking;. 3d Objects \n- 3D Scene understanding\n- 3D Segmentation and Modeling\n#### Video Lectures\n- https://www.youtube.com/watch?v=-nt80JUNwlw\u0026list=PLjMXczUzEYcHvw5YYSU92WrY8IwhTuq7p\u0026index=2 video 9  \n- all videos https://www.coursera.org/learn/stereovision-motion-tracking \n#### Reading Assignments\n##### Google and read the following papers\n- 1. N. Dalal, Histograms of oriented gradients for human detection \n- 2. G. Csurka et al. (Bag of Visual Words - a brilliant representation of cross field research) Visual categorization with bags of keypoints\n- 3. S Lazebnik, C Schmid, J Ponce, Beyond bags of features: Spatial pyramid matching for recognizing natural scene categories \n- 4. Jegou et al. Aggregating local image descriptors into compact codes. \n#### Project\n- Perform Object Segmentation in a 3D Scene with OpenCV\n\n### Part 4: High Level Vision (Features \u003e Analysis)\n\n#### Week 7 (Object Detection \u0026 Classification)\n- Object/scene/activity categorization (semantic segmentation)\n- Object detection (Non max suppression , sliding windows, Boundary boxes and anchors, counting)\n- YOLO and Darknet, region proposal networks \n- Supervised classification algorithms\n- Probabilistic models for sequence data\n- Visual attributes\n- Optical Character Recognition\n- Facial Detection\n#### Video Lectures\n- https://www.youtube.com/watch?v=a-v5_8VGV0A\u0026list=PLjMXczUzEYcHvw5YYSU92WrY8IwhTuq7p\u0026index=8 10-18 \n- my video on YOLO\n#### Reading Assignments\n- http://vision.cs.utexas.edu/376-spring2018/#Tues_May_1 see the associated readings on this page\n#### Project\n- Classify a car in an image with Tensorflow\n\n#### Week 8 (Modern Deep Learning)\n- Active learning\n- Dimensionality reduction\n- Non-parametric methods and big data\n- U-Net\n- Transfer learning\n- Avoiding Overfitting \n- GANs\n\n#### Video Lectures\n- videos 19-20   https://www.youtube.com/watch?v=a-v5_8VGV0A\u0026list=PLjMXczUzEYcHvw5YYSU92WrY8IwhTuq7p\u0026index=8 \n- my video on transfer learning\n- Lectures 1-16 Stanford https://www.youtube.com/watch?v=vT1JzLTH4G4\u0026list=PL3FW7Lu3i5JvHM8ljYj-zLfQRF3EO8sYv \n#### Reading Assignments\n- http://vision.cs.utexas.edu/376-spring2018/#Tues_May_1 see the associated readings on this page\n#### Project\n- Build a Generative Adversarial Network to detect faces\n\n\n\n","funding_links":[],"categories":["Others","Deep Learning"],"sub_categories":["2. Documentation"],"project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2FllSourcell%2FLearn_Computer_Vision","html_url":"https://awesome.ecosyste.ms/projects/github.com%2FllSourcell%2FLearn_Computer_Vision","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2FllSourcell%2FLearn_Computer_Vision/lists"}