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All Projects → bfortuner → Computer Vision

bfortuner / Computer Vision

Computer vision sabbatical study materials

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jupyter-notebookdeep-learningmachine-learningpytorchcomputer-visionopencvjupyterscikit-learn

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Computer Vision

Self-study guide for traditional and ML-based computer vision techniques

Theory

Implementations of important computer vision and machine learning concepts.

Computer Vision

  • Background Subtraction
  • Colorspace
  • Features
  • Filters
  • Geometry
    • Affine transforms
    • Projective transforms
  • HOG Features
  • Histograms
  • Homography
  • Hough Transform
  • Image Gradients
  • K-Means
  • Kalman Filter
  • Linear algebra
    • Vectors
    • Matrices
  • Morphological Operations
  • Optical Flow
  • Segmentation
  • Thresholding

Deep Learning

  • Autoencoder
  • CNN
  • GAN
  • VAE

Applied

Solutions to common tasks with popular libraries: OpenCV, PyTorch, Scikit-learn..

  • Classification
    • ResNet
    • SqueezeNet
  • Object Detection
  • Multi-Object Tracking
    • Ball Tracking
    • Player Tracking
  • Image Processing
    • Grayscale
  • Segmentation
    • FC-DenseNet
    • UNet
  • SfM
    • Image Stitching

Problems

Coding problems and solutions. Mostly computer science fundamentals with a slight focus on computer vision.

  • Arrays
  • Matrix
  • HashMap
  • Stacks/Queues
  • Strings
  • Dynamic Programming
  • LinkedLists
  • Recursion
  • Trees

Papers

Notes on interesting computer vision papers.

Setup

Dependencies

  • Anaconda 3
  • OpenCV 3
  • Pytorch >0.2
  • Tensorflow
  • GPU

Hardware

Datasets

  • Download link for datasets in this repo.

Resources

Courses

Books

Papers

Datasets

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