2013计算机视觉代码合集2

2013计算机视觉代码合集二

原文来自：http://www.yuanyong.org/blog/cv/cv-code-two

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