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Computer Vision source codes

Posted by Hemprasad Y. Badgujar on January 19, 2015


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

  • Caffe – Fast C++ implementation of deep convolutional networks (GPU / CPU / ImageNet 2013 demonstration).
  • OverFeat – C++ library for integrated classification and localization of objects.
  • 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.

Facial Feature Detection and Tracking

  • IntraFace – Very accurate detection and tracking of facial features (C++/Matlab API).

Part-Based Models

Attributes and Semantic Features

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

3D Recognition

Action Recognition

 


 

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

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

Generic Object Recognition

  • ImageNet – Currently the largest visual recognition dataset in terms of number of categories and images.
  • Tiny Images – 80 million 32×32 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

Feature Detection and Description

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

Action Recognition

RGBD Recognition

 

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Getting Started With OpenGL Tutorial

Posted by Hemprasad Y. Badgujar on October 1, 2012


 

 

Getting OpenGL Set Up

In this part, you will learn how to get OpenGL and GLUT set up on Windows, Mac OS X, or Linux, so that you can get started making 3D programs. You can use OpenGL on other operating systems, but this tutorial doesn’t cover how to get it set up on those OSes.

 

 

 

 

 

Posted in Mixed | Tagged: , | Leave a Comment »

 
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