2013计算机视觉代码合集二

申明，本文非笔者原创，本文转载自：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