This is the curriculum for "Learn Computer Vision" by Siraj Raval on Youtube

This is the Curriculum for this 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.

Join the #Computer_Vision_curriculum channel in our Slack channel to find one http://wizards.herokuapp.com

• Video Lectures
• Reading Assignments
• Project(s)

• 8 weeks
• 2-3 Hours of Study per Day

• Python, OpenCV, Tensorflow

• 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

• 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)

• https://www.youtube.com/watch?v=-nt80JUNwlw&list=PLjMXczUzEYcHvw5YYSU92WrY8IwhTuq7p&index=2 videos 1-5

• 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

• Detect an object in an image via the OpenCV Library

• Motion Analysis
• Optical Flow

• 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

• http://szeliski.org/Book/drafts/SzeliskiBook_20100903_draft.pdf Sec 10.5 Sec 8.4 (up until 8.4.1)

• Track a moving object in a video frame with OpenCV

• Segmentation and clustering algorithms like watershed, grabcut
• Interactive segmentation
• Hough transform (detect circles, lines)
• Foreground Extraction

• https://www.youtube.com/watch?v=ZF-3aORwEc0
• https://www.youtube.com/watch?v=3qJej6wgezA

• Sec Sec 5.2-5.4 http://szeliski.org/Book/drafts/SzeliskiBook_20100903_draft.pdf

• Segment Lane lines in a road image with OpenCV

• Fitting lines and curves
• Robust fitting, RANSAC
• Deformable contours

• Videos 6-7 https://www.youtube.com/watch?v=-nt80JUNwlw&list=PLjMXczUzEYcHvw5YYSU92WrY8IwhTuq7p&index=2

• Sec 4.3.2 5.1.1 http://szeliski.org/Book/drafts/SzeliskiBook_20100903_draft.pdf

• Compute Vanishing Points in a hallway image with OpenCV

• Local invariant feature detection and description
• Image transformations and alignment
• Planar homography
• Epipolar geometry and stereo
• Object instance recognition

• https://www.youtube.com/playlist?list=PLyH-5mHPFffFvCCZcbdWXAb_cTy4ZG3Dj

• http://vision.cs.utexas.edu/376-spring2018/#Tues_May_1 see the associated readings on this page

• Turn a set of images into a 3D Object with OpenCV

• Stereo Vision, Dense Motion and Tracking;. 3d Objects
• 3D Scene understanding
• 3D Segmentation and Modeling

• https://www.youtube.com/watch?v=-nt80JUNwlw&list=PLjMXczUzEYcHvw5YYSU92WrY8IwhTuq7p&index=2 video 9
• all videos https://www.coursera.org/learn/stereovision-motion-tracking

• 1. N. Dalal, Histograms of oriented gradients for human detection
• 2. G. Csurka et al. (Bag of Visual Words - a brilliant representation of cross field research) Visual categorization with bags of keypoints
• 3. S Lazebnik, C Schmid, J Ponce, Beyond bags of features: Spatial pyramid matching for recognizing natural scene categories
• 4. Jegou et al. Aggregating local image descriptors into compact codes.

• Perform Object Segmentation in a 3D Scene with OpenCV

• Object/scene/activity categorization (semantic segmentation)
• Object detection (Non max suppression , sliding windows, Boundary boxes and anchors, counting)
• YOLO and Darknet, region proposal networks
• Supervised classification algorithms
• Probabilistic models for sequence data
• Visual attributes
• Optical Character Recognition
• Facial Detection

• https://www.youtube.com/watch?v=a-v5_8VGV0A&list=PLjMXczUzEYcHvw5YYSU92WrY8IwhTuq7p&index=8 10-18
• my video on YOLO

• http://vision.cs.utexas.edu/376-spring2018/#Tues_May_1 see the associated readings on this page

• Classify a car in an image with Tensorflow

• Active learning
• Dimensionality reduction
• Non-parametric methods and big data
• U-Net
• Transfer learning
• Avoiding Overfitting
• GANs

• videos 19-20 https://www.youtube.com/watch?v=a-v5_8VGV0A&list=PLjMXczUzEYcHvw5YYSU92WrY8IwhTuq7p&index=8
• my video on transfer learning
• Lectures 1-16 Stanford https://www.youtube.com/watch?v=vT1JzLTH4G4&list=PL3FW7Lu3i5JvHM8ljYj-zLfQRF3EO8sYv

• http://vision.cs.utexas.edu/376-spring2018/#Tues_May_1 see the associated readings on this page

• Build a Generative Adversarial Network to detect faces