申明，本文非筆者原創，本文轉載自：http://www.yuanyong.org/blog/cv/resource-code

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).

?

Attributes and Semantic Features?

• Relative Attributes?– Modified implementation of RankSVM to train Relative Attributes (ICCV 2011).
• Object Bank?– Implementation of object bank semantic features (NIPS 2010). See also?ActionBank
• Classemes, Picodes, and Meta-class features?– Software for extracting high-level image descriptors (ECCV 2010, NIPS 2011, CVPR 2012).

?

Large-Scale Learning?

• Additive Kernels?– Source code for fast additive kernel SVM classifiers (PAMI 2013).
• LIBLINEAR?– Library for large-scale linear SVM classification.
• VLFeat?– Implementation for Pegasos SVM and Homogeneous Kernel map.

?

Fast Indexing and Image Retrieval?

• FLANN?– Library for performing fast approximate nearest neighbor.
• Kernelized LSH?– Source code for Kernelized Locality-Sensitive Hashing (ICCV 2009).
• ITQ Binary codes?– Code for generation of small binary codes using Iterative Quantization and other baselines such as Locality-Sensitive-Hashing (CVPR 2011).
• INRIA Image Retrieval?– Efficient code for state-of-the-art large-scale image retrieval (CVPR 2011).

?

Object Detection?

• See?Part-based Models?and?Convolutional Nets?above.
• Pedestrian Detection at 100fps?– Very fast and accurate pedestrian detector (CVPR 2012).
• Caltech Pedestrian Detection Benchmark?– Excellent resource for pedestrian detection, with various links for state-of-the-art implementations.
• OpenCV?– Enhanced implementation of Viola&Jones real-time object detector, with trained models for face detection.
• Efficient Subwindow Search?– Source code for branch-and-bound optimization for efficient object localization (CVPR 2008).

?

3D Recognition?

• Point-Cloud Library?– Library for 3D image and point cloud processing.

?

Action Recognition?

• ActionBank?– Source code for action recognition based on the ActionBank representation (CVPR 2012).
• STIP Features?– software for computing space-time interest point descriptors
• Independent Subspace Analysis?– Look for Stacked ISA for Videos (CVPR 2011)
• Velocity Histories of Tracked Keypoints?- C++ code for activity recognition using the velocity histories of tracked keypoints (ICCV 2009)

---

Datasets

?

Attributes?

• Animals with Attributes?– 30,475 images of 50 animals classes with 6 pre-extracted feature representations for each image.
• aYahoo and aPascal?– Attribute annotations for images collected from Yahoo and Pascal VOC 2008.
• FaceTracer?– 15,000 faces annotated with 10 attributes and fiducial points.
• PubFig?– 58,797 face images of 200 people with 73 attribute classifier outputs.
• LFW?– 13,233 face images of 5,749 people with 73 attribute classifier outputs.
• Human Attributes?– 8,000 people with annotated attributes. Check also this?link?for another dataset of human attributes.
• SUN Attribute Database?– Large-scale scene attribute database with a taxonomy of 102 attributes.
• ImageNet Attributes?– Variety of attribute labels for the ImageNet dataset.
• Relative attributes?– Data for OSR and a subset of PubFig datasets. Check also this?link?for the WhittleSearch data.
• Attribute Discovery Dataset?– Images of shopping categories associated with textual descriptions.

?

Fine-grained Visual Categorization?

• Caltech-UCSD Birds Dataset?– Hundreds of bird categories with annotated parts and attributes.
• Stanford Dogs Dataset?– 20,000 images of 120 breeds of dogs from around the world.
• Oxford-IIIT Pet Dataset?– 37 category pet dataset with roughly 200 images for each class. Pixel level trimap segmentation is included.
• Leeds Butterfly Dataset?– 832 images of 10 species of butterflies.
• Oxford Flower Dataset?– Hundreds of flower categories.

?

Face Detection?

• FDDB?– UMass face detection dataset and benchmark (5,000+ faces)
• CMU/MIT?– Classical face detection dataset.

?

Face Recognition?

• Face Recognition Homepage?– Large collection of face recognition datasets.
• LFW?– UMass unconstrained face recognition dataset (13,000+ face images).
• NIST Face Homepage?– includes face recognition grand challenge (FRGC), vendor tests (FRVT) and others.
• CMU Multi-PIE?– contains more than 750,000 images of 337 people, with 15 different views and 19 lighting conditions.
• FERET?– Classical face recognition dataset.
• Deng Cai’s face dataset in Matlab Format?– Easy to use if you want play with simple face datasets including Yale, ORL, PIE, and Extended Yale B.
• SCFace?– Low-resolution face dataset captured from surveillance cameras.

?

Handwritten Digits?

• MNIST?– large dataset containing a training set of 60,000 examples, and a test set of 10,000 examples.

?

Pedestrian Detection

• Caltech Pedestrian Detection Benchmark?– 10 hours of video taken from a vehicle,350K bounding boxes for about 2.3K unique pedestrians.
• INRIA Person Dataset?– Currently one of the most popular pedestrian detection datasets.
• ETH Pedestrian Dataset?– Urban dataset captured from a stereo rig mounted on a stroller.
• TUD-Brussels Pedestrian Dataset?– Dataset with image pairs recorded in an crowded urban setting with an onboard camera.
• PASCAL Human Detection?– One of 20 categories in PASCAL VOC detection challenges.
• USC Pedestrian Dataset?– Small dataset captured from surveillance cameras.

?

Generic Object Recognition?

• ImageNet?– Currently the largest visual recognition dataset in terms of number of categories and images.
• Tiny Images?– 80 million 32x32 low resolution images.
• Pascal VOC?– One of the most influential visual recognition datasets.
• Caltech 101?/?Caltech 256?– Popular image datasets containing 101 and 256 object categories, respectively.
• MIT LabelMe?– Online annotation tool for building computer vision databases.

?

Scene Recognition

• MIT SUN Dataset?– MIT scene understanding dataset.
• UIUC Fifteen Scene Categories?– Dataset of 15 natural scene categories.

?

Feature Detection and Description?

• VGG Affine Dataset?– Widely used dataset for measuring performance of feature detection and description. CheckVLBenchmarks?for an evaluation framework.

?

Action Recognition

• Benchmarking Activity Recognition?– CVPR 2012 tutorial covering various datasets for action recognition.

?

RGBD Recognition?

• RGB-D Object Dataset?– Dataset containing 300 common household objects

?

Reference:

[1]:?http://rogerioferis.com/VisualRecognitionAndSearch/Resources.html

轉載于:https://www.cnblogs.com/huty/p/8518871.html

總結

以上是生活随笔為你收集整理的2013计算机视觉代码合集二的全部內容，希望文章能夠幫你解決所遇到的問題。

如果覺得生活随笔網站內容還不錯，歡迎將生活随笔推薦給好友。