机器视觉开源代码集合（转载）
http://xilinx.eetrend.com/article/8919一、特征提取Feature Extraction：
[*]SIFT [Demo program][SIFT Library] [VLFeat]
[*]PCA-SIFT [Project]
[*]Affine-SIFT [Project]
[*]SURF [OpenSURF] [Matlab Wrapper]
[*]Affine Covariant Features [Oxford project]
[*]MSER [Oxford project] [VLFeat]
[*]Geometric Blur [Code]
[*]Local Self-Similarity Descriptor [Oxford implementation]
[*]Global and Efficient Self-Similarity [Code]
[*]Histogram of Oriented Graidents [INRIA Object Localization Toolkit] [OLT toolkit for Windows]
[*]GIST [Project]
[*]Shape Context [Project]
[*]Color Descriptor [Project]
[*]Pyramids of Histograms of Oriented Gradients [Code]
[*]Space-Time Interest Points (STIP) [Project] [Code]
[*]Boundary Preserving Dense Local Regions [Project]
[*]Weighted Histogram[Code]
[*]Histogram-based Interest Points Detectors[Paper][Code]
[*]An OpenCV - C++ implementation of Local Self Similarity Descriptors [Project]
[*]Fast Sparse Representation with Prototypes[Project]
[*]Corner Detection [Project]
[*]AGAST Corner Detector: faster than FAST and even FAST-ER[Project]
[*]Real-time Facial Feature Detection using Conditional Regression Forests[Project]
[*]Global and Efficient Self-Similarity for Object Classification and Detection[code]
[*]WαSH: Weighted α-Shapes for Local Feature Detection[Project]
[*]HOG[Project]
[*]Online Selection of Discriminative Tracking Features[Project]
二、图像分割Image Segmentation：
[*]Normalized Cut [Matlab code]
[*]Gerg Mori’ Superpixel code [Matlab code]
[*]Efficient Graph-based Image Segmentation [C++ code] [Matlab wrapper]
[*]Mean-Shift Image Segmentation [EDISON C++ code] [Matlab wrapper]
[*]OWT-UCM Hierarchical Segmentation [Resources]
[*]Turbepixels [Matlab code 32bit] [Matlab code 64bit] [Updated code]
[*]Quick-Shift [VLFeat]
[*]SLIC Superpixels [Project]
[*]Segmentation by Minimum Code Length [Project]
[*]Biased Normalized Cut [Project]
[*]Segmentation Tree [Project]
[*]Entropy Rate Superpixel Segmentation [Code]
[*]Fast Approximate Energy Minimization via Graph Cuts[Paper][Code]
[*]Efficient Planar Graph Cuts with Applications in Computer Vision[Paper][Code]
[*]Isoperimetric Graph Partitioning for Image Segmentation[Paper][Code]
[*]Random Walks for Image Segmentation[Paper][Code]
[*]Blossom V: A new implementation of a minimum cost perfect matching algorithm[Code]
[*]An Experimental Comparison of Min-Cut/Max-Flow Algorithms for Energy Minimization in Computer Vision[Paper][Code]
[*]Geodesic Star Convexity for Interactive Image Segmentation[Project]
[*]Contour Detection and Image Segmentation Resources[Project][Code]
[*]Biased Normalized Cuts[Project]
[*]Max-flow/min-cut[Project]
[*]Chan-Vese Segmentation using Level Set[Project]
[*]A Toolbox of Level Set Methods[Project]
[*]Re-initialization Free Level Set Evolution via Reaction Diffusion[Project]
[*]Improved C-V active contour model[Paper][Code]
[*]A Variational Multiphase Level Set Approach to Simultaneous Segmentation and Bias Correction[Paper][Code]
[*]Level Set Method Research by Chunming Li[Project]
[*]ClassCut for Unsupervised Class Segmentation[code]
[*]SEEDS: Superpixels Extracted via Energy-Driven Sampling ][other]
三、目标检测Object Detection：
[*]A simple object detector with boosting [Project]
[*]INRIA Object Detection and Localization Toolkit [Project]
[*]Discriminatively Trained Deformable Part Models [Project]
[*]Cascade Object Detection with Deformable Part Models [Project]
[*]Poselet [Project]
[*]Implicit Shape Model [Project]
[*]Viola and Jones’s Face Detection [Project]
[*]Bayesian Modelling of Dyanmic Scenes for Object Detection[Paper][Code]
[*]Hand detection using multiple proposals[Project]
[*]Color Constancy, Intrinsic Images, and Shape Estimation[Paper][Code]
[*]Discriminatively trained deformable part models[Project]
[*]Gradient Response Maps for Real-Time Detection of Texture-Less Objects: LineMOD [Project]
[*]Image Processing On Line[Project]
[*]Robust Optical Flow Estimation[Project]
[*]Where's Waldo: Matching People in Images of Crowds[Project]
[*]Scalable Multi-class Object Detection[Project]
[*]Class-Specific Hough Forests for Object Detection[Project]
[*]Deformed Lattice Detection In Real-World Images[Project]
[*]Discriminatively trained deformable part models[Project]
四、显著性检测Saliency Detection：
[*]Itti, Koch, and Niebur’ saliency detection [Matlab code]
[*]Frequency-tuned salient region detection [Project]
[*]Saliency detection using maximum symmetric surround [Project]
[*]Attention via Information Maximization [Matlab code]
[*]Context-aware saliency detection [Matlab code]
[*]Graph-based visual saliency [Matlab code]
[*]Saliency detection: A spectral residual approach. [Matlab code]
[*]Segmenting salient objects from images and videos. [Matlab code]
[*]Saliency Using Natural statistics. [Matlab code]
[*]Discriminant Saliency for Visual Recognition from Cluttered Scenes. [Code]
[*]Learning to Predict Where Humans Look [Project]
[*]Global Contrast based Salient Region Detection [Project]
[*]Bayesian Saliency via Low and Mid Level Cues[Project]
[*]Top-Down Visual Saliency via Joint CRF and Dictionary Learning[Paper][Code]
[*]Saliency Detection: A Spectral Residual Approach[Code]
五、图像分类、聚类Image Classification, Clustering
[*]Pyramid Match [Project]
[*]Spatial Pyramid Matching [Code]
[*]Locality-constrained Linear Coding [Project] [Matlab code]
[*]Sparse Coding [Project] [Matlab code]
[*]Texture Classification [Project]
[*]Multiple Kernels for Image Classification [Project]
[*]Feature Combination [Project]
[*]SuperParsing [Code]
[*]Large Scale Correlation Clustering Optimization[Matlab code]
[*]Detecting and Sketching the Common[Project]
[*]Self-Tuning Spectral Clustering[Project][Code]
[*]User Assisted Separation of Reflections from a Single Image Using a Sparsity Prior[Paper][Code]
[*]Filters for Texture Classification[Project]
[*]Multiple Kernel Learning for Image Classification[Project]
[*]SLIC Superpixels[Project]
六、抠图Image Matting
[*]A Closed Form Solution to Natural Image Matting [Code]
[*]Spectral Matting [Project]
[*]Learning-based Matting [Code]
七、目标跟踪Object Tracking：
[*]A Forest of Sensors - Tracking Adaptive Background Mixture Models [Project]
[*]Object Tracking via Partial Least Squares Analysis[Paper][Code]
[*]Robust Object Tracking with Online Multiple Instance Learning[Paper][Code]
[*]Online Visual Tracking with Histograms and Articulating Blocks[Project]
[*]Incremental Learning for Robust Visual Tracking[Project]
[*]Real-time Compressive Tracking[Project]
[*]Robust Object Tracking via Sparsity-based Collaborative Model[Project]
[*]Visual Tracking via Adaptive Structural Local Sparse Appearance Model[Project]
[*]Online Discriminative Object Tracking with Local Sparse Representation[Paper][Code]
[*]Superpixel Tracking[Project]
[*]Learning Hierarchical Image Representation with Sparsity, Saliency and Locality[Paper][Code]
[*]Online Multiple Support Instance Tracking [Paper][Code]
[*]Visual Tracking with Online Multiple Instance Learning[Project]
[*]Object detection and recognition[Project]
[*]Compressive Sensing Resources[Project]
[*]Robust Real-Time Visual Tracking using Pixel-Wise Posteriors[Project]
[*]Tracking-Learning-Detection[Project][OpenTLD/C++ Code]
[*]the HandVu：vision-based hand gesture interface[Project]
[*]Learning Probabilistic Non-Linear Latent Variable Models for Tracking Complex Activities[Project]
八、Kinect：
[*]Kinect toolbox[Project]
[*]OpenNI[Project]
[*]zouxy09 CSDN Blog[Resource]
[*]FingerTracker 手指跟踪[code]
九、3D相关：
[*]3D Reconstruction of a Moving Object[Paper] [Code]
[*]Shape From Shading Using Linear Approximation[Code]
[*]Combining Shape from Shading and Stereo Depth Maps[Project][Code]
[*]Shape from Shading: A Survey[Paper][Code]
[*]A Spatio-Temporal Descriptor based on 3D Gradients (HOG3D)[Project][Code]
[*]Multi-camera Scene Reconstruction via Graph Cuts[Paper][Code]
[*]A Fast Marching Formulation of Perspective Shape from Shading under Frontal Illumination[Paper][Code]
[*]Reconstruction:3D Shape, Illumination, Shading, Reflectance, Texture[Project]
[*]Monocular Tracking of 3D Human Motion with a Coordinated Mixture of Factor Analyzers[Code]
[*]Learning 3-D Scene Structure from a Single Still Image[Project]
十、机器学习算法：
[*]Matlab class for computing Approximate Nearest Nieghbor (ANN) [Matlab class providing interface toANN library]
[*]Random Sampling[code]
[*]Probabilistic Latent Semantic Analysis (pLSA)[Code]
[*]FASTANN and FASTCLUSTER for approximate k-means (AKM)[Project]
[*]Fast Intersection / Additive Kernel SVMs[Project]
[*]SVM[Code]
[*]Ensemble learning[Project]
[*]Deep Learning[Net]
[*]Deep Learning Methods for Vision[Project]
[*]Neural Network for Recognition of Handwritten Digits[Project]
[*]Training a deep autoencoder or a classifier on MNIST digits[Project]
[*]THE MNIST DATABASE of handwritten digits[Project]
[*]Ersatz：deep neural networks in the cloud[Project]
[*]Deep Learning [Project]
[*]sparseLM : Sparse Levenberg-Marquardt nonlinear least squares in C/C++[Project]
[*]Weka 3: Data Mining Software in Java[Project]
[*]Invited talk "A Tutorial on Deep Learning" by Dr. Kai Yu (余凯)[Video]
[*]CNN - Convolutional neural network class[Matlab Tool]
[*]Yann LeCun's Publications[Wedsite]
[*]LeNet-5, convolutional neural networks[Project]
[*]Training a deep autoencoder or a classifier on MNIST digits[Project]
[*]Deep Learning 大牛Geoffrey E. Hinton's HomePage[Website]
[*]Multiple Instance Logistic Discriminant-based Metric Learning (MildML) and Logistic Discriminant-based Metric Learning (LDML)[Code]
[*]Sparse coding simulation software[Project]
[*]Visual Recognition and Machine Learning Summer School[Software]
十一、目标、行为识别Object, Action Recognition：
[*]Action Recognition by Dense Trajectories[Project][Code]
[*]Action Recognition Using a Distributed Representation of Pose and Appearance[Project]
[*]Recognition Using Regions[Paper][Code]
[*]2D Articulated Human Pose Estimation[Project]
[*]Fast Human Pose Estimation Using Appearance and Motion via Multi-Dimensional Boosting Regression[Paper][Code]
[*]Estimating Human Pose from Occluded Images[Paper][Code]
[*]Quasi-dense wide baseline matching[Project]
[*]ChaLearn Gesture Challenge: Principal motion: PCA-based reconstruction of motion histograms[Project]
[*]Real Time Head Pose Estimation with Random Regression Forests[Project]
[*]2D Action Recognition Serves 3D Human Pose Estimation[
[*]A Hough Transform-Based Voting Framework for Action Recognition[
[*]Motion Interchange Patterns for Action Recognition in Unconstrained Videos[
[*]2D articulated human pose estimation software[Project]
[*]Learning and detecting shape models [code]
[*]Progressive Search Space Reduction for Human Pose Estimation[Project]
[*]Learning Non-Rigid 3D Shape from 2D Motion[Project]
十二、图像处理：
[*]Distance Transforms of Sampled Functions[Project]
[*]The Computer Vision Homepage[Project]
[*]Efficient appearance distances between windows[code]
[*]Image Exploration algorithm[code]
[*]Motion Magnification 运动放大 [Project]
[*]Bilateral Filtering for Gray and Color Images 双边滤波器 [Project]
[*]A Fast Approximation of the Bilateral Filter using a Signal Processing Approach [
十三、一些实用工具：
[*]EGT: a Toolbox for Multiple View Geometry and Visual Servoing[Project] [Code]
[*]a development kit of matlab mex functions for OpenCV library[Project]
[*]Fast Artificial Neural Network Library[Project]
十四、人手及指尖检测与识别：
[*]finger-detection-and-gesture-recognition [Code]
[*]Hand and Finger Detection using JavaCV[Project]
[*]Hand and fingers detection[Code]
十五、场景解释：
[*]Nonparametric Scene Parsing via Label Transfer [Project]
十六、光流Optical flow：
[*]High accuracy optical flow using a theory for warping [Project]
[*]Dense Trajectories Video Description [Project]
[*]SIFT Flow: Dense Correspondence across Scenes and its Applications[Project]
[*]KLT: An Implementation of the Kanade-Lucas-Tomasi Feature Tracker [Project]
[*]Tracking Cars Using Optical Flow[Project]
[*]Secrets of optical flow estimation and their principles[Project]
[*]implmentation of the Black and Anandan dense optical flow method[Project]
[*]Optical Flow Computation[Project]
[*]Beyond Pixels: Exploring New Representations and Applications for Motion Analysis[Project]
[*]A Database and Evaluation Methodology for Optical Flow[Project]
[*]optical flow relative[Project]
[*]Robust Optical Flow Estimation [Project]
[*]optical flow[Project]
十七、图像检索Image Retrieval：
[*]Semi-Supervised Distance Metric Learning for Collaborative Image Retrieval ][code]
十八、马尔科夫随机场Markov Random Fields：
[*]Markov Random Fields for Super-Resolution [Project]
[*]A Comparative Study of Energy Minimization Methods for Markov Random Fields with Smoothness-Based Priors [Project]
十九、运动检测Motion detection：
[*]Moving Object Extraction, Using Models or Analysis of Regions [Project]
[*]Background Subtraction: Experiments and Improvements for ViBe [Project]
[*]A Self-Organizing Approach to Background Subtraction for Visual Surveillance Applications [Project]
[*]changedetection.net: A new change detection benchmark dataset[Project]
[*]ViBe - a powerful technique for background detection and subtraction in video sequences[Project]
[*]Background Subtraction Program[Project]
[*]Motion Detection Algorithms[Project]
[*]Stuttgart Artificial Background Subtraction Dataset[Project]
[*]Object Detection, Motion Estimation, and Tracking[Project]
Feature Detection and DescriptionGeneral 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)
DatasetsAttributes
[*]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.
[*]www.cs.umass.edu/lfw/]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
[*]www.cs.umass.edu/fddb/]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.
[*]www.cs.umass.edu/lfw/]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. CheckVLBenchmarksfor 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:: http://rogerioferis.com/VisualRecognitionAndSearch/Resources.html
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