Resources
- Forsyth and Ponce, Computer Vision: A Modern Approach
- Richard Hartley and Andrew Zisserman, Multiple View Geometry in Computer Vision
- Kristen Grauman and Bastian Leibe, short book on Visual Object Recognition
- Christopher M. Bishop, Pattern Recognition and Machine Learning
- Tom Mitchell, Machine Learning
- Short tutorial on getting started with Matlab
- CV online: wiki with computer vision concepts
- Li Fei-Fei, Rob Fergus, Antonio Torralba, Tutorial on Recognizing and Learning Object Categories
- Andrew Moore, Tutorial on Support Vector Machines
- Sebastian Nowozin, Christoph H. Lampert, Structured Learning and Prediction in Computer Vision (advanced Machine Learning Tutorial)
- Tutorial on writing fast Matlab code
- OpenCV library: open source computer vision library (c++)
- VLfeat: open source computer vision library (Matlab, c)
- LIBSVM: A Library for Support Vector Machines (Matlab, Python)
- Structured Edge Detection Toolbox: Very fast edge detector (up to 60 fps) with high accuracy
- Object detection code with Deformable Part-based Models
- Caffe: Deep learning features for image classification
- Bundler: Structure from Motion (SfM) for Unordered Image Collections
- SLIC superpixels: Very fast superpixel code
- UCM superpixels: Accurate superpixels
- Selective Search: code for computing bottom-up region proposals
- PASCAL VOC: Object recognition dataset
- KITTI: Autonomous driving dataset
- Reconstruction Meets Recognition Challenge (RMRC): Indoor recognition with RGB-D data
- LabelMe: Open image annotation tool
- ImageNet: Large-scale object dataset
- ReKognition API: Demo for face recognition and detection, image classification
- Photosynth: View the world in 3D
- Google goggles app
- Clothing parser demo by Tamara Berg’s group
- A list of online computer vision demos
- The Computer Vision Industry, a list of CV companies maintained by David Lowe