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2013计算机视觉代码合集二

作者:cvchina | 分类:图像处理 | 标签:目标检测 源码 SIFT 

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: 

 

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 

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