Feature Detection and Description

General Libraries: 

• VLFeat – Implementation of various feature descriptors (including SIFT, HOG, and LBP) and covariant feature detectors (including DoG, Hessian, Harris Laplace, Hessian Laplace, Multiscale Hessian, Multiscale Harris). Easy-to-use Matlab interface. See Modern features: Software – Slides providing a demonstration of VLFeat and also links to other software. Check also VLFeat hands-on session training

• OpenCV – Various implementations of modern feature detectors and descriptors (SIFT, SURF, FAST, BRIEF, ORB, FREAK, etc.)

Fast Keypoint Detectors for Real-time Applications: 

• FAST – High-speed corner detector implementation for a wide variety of platforms

• AGAST – Even faster than the FAST corner detector. A multi-scale version of this method is used for the BRISK descriptor (ECCV 2010).

Binary Descriptors for Real-Time Applications: 

• BRIEF – C++ code for a fast and accurate interest point descriptor (not invariant to rotations and scale) (ECCV 2010)

• ORB – OpenCV implementation of the Oriented-Brief (ORB) descriptor (invariant to rotations, but not scale)

• BRISK – Efficient Binary descriptor invariant to rotations and scale. It includes a Matlab mex interface. (ICCV 2011)

• FREAK – Faster than BRISK (invariant to rotations and scale) (CVPR 2012)

SIFT and SURF Implementations: 

• SIFT: VLFeat, OpenCV, Original code by David Lowe, GPU implementation, OpenSIFT

• SURF: Herbert Bay’s code, OpenCV, GPU-SURF

Other Local Feature Detectors and Descriptors: 

• VGG Affine Covariant features – Oxford code for various affine covariant feature detectors and descriptors.

• LIOP descriptor – Source code for the Local Intensity order Pattern (LIOP) descriptor (ICCV 2011).

• Local Symmetry Features – Source code for matching of local symmetry features under large variations in lighting, age, and rendering style (CVPR 2012).

Global Image Descriptors: 

• GIST – Matlab code for the GIST descriptor

• CENTRIST – Global visual descriptor for scene categorization and object detection (PAMI 2011)

Feature Coding and Pooling 

• VGG Feature Encoding Toolkit – Source code for various state-of-the-art feature encoding methods – including Standard hard encoding, Kernel codebook encoding, Locality-constrained linear encoding, and Fisher kernel encoding.

• Spatial Pyramid Matching – Source code for feature pooling based on spatial pyramid matching (widely used for image classification)

Convolutional Nets and Deep Learning 

• EBLearn – C++ Library for Energy-Based Learning. It includes several demos and step-by-step instructions to train classifiers based on convolutional neural networks.

• Torch7 – Provides a matlab-like environment for state-of-the-art machine learning algorithms, including a fast implementation of convolutional neural networks.

• Deep Learning - Various links for deep learning software.

Part-Based Models 

• Deformable Part-based Detector – Library provided by the authors of the original paper (state-of-the-art in PASCAL VOC detection task)

• Efficient Deformable Part-Based Detector – Branch-and-Bound implementation for a deformable part-based detector.

• Accelerated Deformable Part Model – Efficient implementation of a method that achieves the exact same performance of deformable part-based detectors but with significant acceleration (ECCV 2012).

• Coarse-to-Fine Deformable Part Model – Fast approach for deformable object detection (CVPR 2011).

• Poselets – C++ and Matlab versions for object detection based on poselets.

• Part-based Face Detector and Pose Estimation – Implementation of a unified approach for face detection, pose estimation, and landmark localization (CVPR 2012).

2013计算机视觉代码合集二