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總結一些機器視覺庫

本文轉載自   查看原文   2016-11-17 21:50   7357    總結概述/ 機器學習 Machine learning

一、傳統圖像算法工程師：
主要涉及圖形處理，包括形態學、圖像質量、相機成像之3A算法、去霧處理、顏色空間轉換、濾鏡等，主要在安防公司或者機器視覺領域，包括缺陷檢測；

二、現代圖像算法工程師:
涉及模式識別，主要表現的經驗為Adaboost、SVM的研究與應用，特征選取與提取，包括智能駕駛的研究與應用、行人檢測、人臉識別；

三、人工智能時代圖像算法工程師：
深度學習，主要在大型互聯網公司或者研究所機構，具體體現在TensorFlow等開源庫的研究與應用，包括機器人的研、基於深度學習的人臉識別；

CSDN上也總結了很多：（補充2016-12-21  23:49:53）

                      Deep Learning源代碼收集-持續更新…

                      Deep Learning（深度學習）

                      計算機視覺、機器學習相關領域論文和源代碼大集合--持續更新……

                      圖像處理與計算機視覺：基礎，經典以及最近發展  （經典論文集合）

                     計算機視覺領域的一些牛人博客，超有實力的研究機構web主頁（轉）

                     來自西弗吉利亞大學li xin整理的CV代碼合集（轉）

                     圖像處理與機器視覺行業分析

                     計算機視覺整理庫

                     https://github.com/ty4z2008/Qix

通用庫/General Library

  • RAVL  Recognition And Vision Library. 線程安全。強大的IO機制。包含AAM。
  • CImg  很酷的一個圖像處理包。整個庫只有一個頭文件。包含一個基於PDE的光流算法。

圖像，視頻IO/Image, Video IO

AR相關/Augmented Reality

  • ARToolKitPlus   ARToolKit的增強版。實現了更好的姿態估計算法。
  • PTAM    實時的跟蹤、SLAM、AR庫。無需Marker，模板，內置傳感器等。
  • BazAR   基於特征點檢測和識別的AR庫。

局部不變特征/Local Invariant Feature

  • VLFeat   目前最好的Sift開源實現。同時包含了KD-tree，KD-Forest，BoW實現。
  • VLFeat：著名而常用

    項目網站：http://www.vlfeat.org

    著名的計算機視覺/圖像處理開源項目，知名度應該不必OpenCV低太多，曾獲ACM Open Source Software Competition 2010一等獎。使用C語言編寫，提供C語言和Matlab兩種接口。實現了大量計算機視覺算法，包括：

    • 常用圖像處理功能，包括顏色空間變換、幾何變換（作為Matlab的補充），常用機器學習算法，包括GMM、SVM、KMeans等，常用的圖像處理的plot工具。

    • 特征提取，包括 Covariant detectors, HOG, SIFT,MSER等。VLFeat提供了一個vl_covdet() 函數作為框架，可以方便的統一所謂“co-variant feature detectors”，包括了DoG, Harris-Affine, Harris-Laplace並且可以提取SIFT或raw patches描述子。

    • 超像素（Superpixel）分割，包括常用的Quick shift, SLIC算法等

    • 高級聚類算法，比如整數KMeans：Integer k-means (IKM)、hierarchical version of integer k-means (HIKM)，基於互信息自動判定聚類類數的算法Agglomerative Information Bottleneck (AIB) algorithm等

    • 高維特特征匹配算法，隨機KD樹Randomized kd-trees

    可以在這里查看VLFeat完整的功能列表。 http://www.vlfeat.org/matlab/matlab.html

  • Ferns    基於Naive Bayesian Bundle的特征點識別。高速，但占用內存高。

目標檢測/Object Detection

（近似）最近鄰/ANN

  • FLANN    目前最完整的（近似）最近鄰開源庫。不但實現了一系列查找算法，還包含了一種自動選取最快算法的機制。
  • ANN    另外一個近似最近鄰庫。

SLAM & SFM

  • SceneLib    [LGPL]   monoSLAM庫。由Androw Davison開發。

圖像分割/Segmentation

  • SLIC Super Pixel   使用Simple Linear Iterative Clustering產生指定數目，近似均勻分布的Super Pixel。

目標跟蹤/Tracking

  • TLD   基於Online Random Forest的目標跟蹤算法。
  • KLT  Kanade-Lucas-Tracker

直線檢測/Line Detection

  • DSCC    基於聯通域連接的直線檢測算法。
  • LSD [GPL]   基於梯度的，局部直線段檢測算子。

指紋/Finger Print

  • pHash [GPL]   基於感知的多媒體文件Hash算法。（提取，對比圖像、視頻、音頻的指紋）

視覺顯著性/Visual Salience

FFT/DWT

  • FFTW [GPL]   最快，最好的開源FFT。
  • FFTReal [WTFPL]   輕量級的FFT實現。許可證是亮點。

音頻處理/Audio processing

  • STK [Free]   音頻處理，音頻合成。

小波變換   快速小波變換（FWT）

BRIEF: Binary Robust Independent Elementary Feature 一個很好的局部特征描述子，里面有FAST corner + BRIEF實現特征點匹配的DEMO：http://cvlab.epfl.ch/software/brief/

http://code.google.com/p/javacv

Java打包的OpenCV, FFmpeg, libdc1394, PGR FlyCapture, OpenKinect, videoInput, and ARToolKitPlus庫。可以放在Android上用~

 libHIK,HIK SVM，計算HIK SVM跟Centrist的Lib。http://c2inet.sce.ntu.edu.sg/Jianxin/projects/libHIK/libHIK.htm

 一組視覺顯著性檢測代碼的鏈接：http://cg.cs.tsinghua.edu.cn/people/~cmm/saliency/

 

Peter Kovesi的工具箱：輕量好用，側重圖像處理  http://www.peterkovesi.com/matlabfns/

項目網站：http://www.csse.uwa.edu.au/~pk/research/matlabfns/

這位Peter大哥目前在The University of Western Australia工作，他自己寫了一套Matlab計算機視覺算法，所謂工具箱其實就是許多m文件的集合，全部Matlab實現，無需編譯安裝，支持Octave（如果沒有Matlab的話，有了這個工具箱也可以在Octave下進行圖像處理了）。別看這位大哥單槍匹馬，人家的工具箱可是相當有名，研究時候需要哪個Matlab的計算機視覺小功能，直接到他家主頁上下幾個m文件放在自己文件夾就好了。這個工具箱主要以圖像處理算法為主，附帶一些三維視覺的基本算法，列一些包括的功能：

MexOpenCV：讓Matlab支持調用的OpenCV

項目網站：http://www.cs.sunysb.edu/~kyamagu/mexopencv/

作者Kota Yamaguchi桑是石溪大學（Stony Brook University）的PhD，早些時候自己搞了一套東西把OpenCV的代碼編譯成Matlab可用的mex接口，然后這個東西迅速火了。今年夏天這個項目被OpenCV吸收為一個模塊，貌似是搞了一個Google Summer of Code（GSoC）的項目，最近（大概是9、10月）已經merge到了OpenCV主包，有興趣的可以到Github的OpenCV庫下的module/matlab去玩一下，應該會在10月份的OpenCV 3 alpha里正式發布。現在OpenCV就同時有了Python和Maltab的binding（好強大）。具體的功能就不細說了，既然是OpenCV的binding，當然是可以使用OpenCV的絕大多數算法了。

 

介紹n款計算機視覺庫/人臉識別開源庫/軟件

計算機視覺庫 OpenCV

OpenCV是Intel®開源計算機視覺庫。它由一系列 C 函數和少量 C++ 類構成，實現了圖像處理和計算機視覺方面的很多通用算法。 OpenCV 擁有包括 300 多個C函數的跨平台的中、高層 API。它不依賴於其它的外部庫——盡管也可以使用某些外部庫。 OpenCV 對非商業...

人臉識別 faceservice.cgi

faceservice.cgi 是一個用來進行人臉識別的 CGI 程序， 你可以通過上傳圖像，然后該程序即告訴你人臉的大概坐標位置。faceservice是采用 OpenCV 庫進行開發的。

OpenCV的.NET版 OpenCVDotNet

OpenCVDotNet 是一個 .NET 對 OpenCV 包的封裝。

人臉檢測算法 jViolajones

jViolajones是人臉檢測算法Viola-Jones的一個Java實現，並能夠加載OpenCV XML文件。 示例代碼：http://www.oschina.net/code/snippet_12_2033

Java視覺處理庫 JavaCV

JavaCV 提供了在計算機視覺領域的封裝庫，包括：OpenCV、ARToolKitPlus、libdc1394 2.x 、PGR FlyCapture和FFmpeg。此外，該工具可以很容易地使用Java平台的功能。 JavaCV還帶有硬件加速的全屏幕圖像顯示（CanvasFrame），易於在多個內核中執行並行代碼（並...

運動檢測程序 QMotion

QMotion 是一個采用 OpenCV 開發的運動檢測程序，基於 QT。

視頻監控系統 OpenVSS

OpenVSS - 開放平台的視頻監控系統 - 是一個系統級別的視頻監控軟件視頻分析框架（VAF）的視頻分析與檢索和播放服務，記錄和索引技術。它被設計成插件式的支持多攝像頭平台，多分析儀模塊（OpenCV的集成），以及多核心架構。

手勢識別 hand-gesture-detection

手勢識別，用OpenCV實現

人臉檢測識別 mcvai-tracking

提供人臉檢測、識別與檢測特定人臉的功能，示例代碼 cvReleaseImage( &gray ); cvReleaseMemStorage(&storage); cvReleaseHaarClassifierCascade(&cascade);...

人臉檢測與跟蹤庫 asmlibrary

Active Shape Model Library (ASMLibrary©) SDK, 用OpenCV開發，用於人臉檢測與跟蹤。

Lua視覺開發庫 libecv

ECV 是 lua 的計算機視覺開發庫(目前只提供linux支持)

OpenCV的.Net封裝 OpenCVSharp

OpenCVSharp 是一個OpenCV的.Net wrapper，應用最新的OpenCV庫開發，使用習慣比EmguCV更接近原始的OpenCV，有詳細的使用樣例供參考。

3D視覺庫 fvision2010

基於OpenCV構建的圖像處理和3D視覺庫。 示例代碼： ImageSequenceReaderFactory factory; ImageSequenceReader* reader = factory.pathRegex("c:/a/im_%03d.jpg", 0, 20); //ImageSequenceReader*
reader = factory.avi("a.avi"); if (reader == NULL) { ...

基於QT的計算機視覺庫 QVision

基於 QT 的面向對象的多平台計算機視覺庫。可以方便的創建圖形化應用程序，算法庫主要從 OpenCV，GSL，CGAL，IPP，Octave 等高性能庫借鑒而來。

圖像特征提取 cvBlob

cvBlob 是計算機視覺應用中在二值圖像里尋找連通域的庫.能夠執行連通域分析與特征提取.

實時圖像/視頻處理濾波開發包 GShow

GShow is a real-time image/video processing filter development kit. It successfully integrates DirectX11 with DirectShow framework. So it has the following
features: GShow 是實時 圖像/視頻 處理濾波開發包，集成DiretX11。...

視頻捕獲 API VideoMan

VideoMan 提供一組視頻捕獲 API 。支持多種視頻流同時輸入（視頻傳輸線、USB攝像頭和視頻文件等）。能利用 OpenGL 對輸入進行處理，方便的與 OpenCV，CUDA 等集成開發計算機視覺系統。

開放模式識別項目 OpenPR

Pattern Recognition project（開放模式識別項目），致力於開發出一套包含圖像處理、計算機視覺、自然語言處理、模式識別、機器學習和相關領域算法的函數庫。

OpenCV的Python封裝 pyopencv

OpenCV的Python封裝，主要特性包括： 提供與OpenCV 2.x中最新的C++接口極為相似的Python接口，並且包括C++中不包括的C接口 提供對OpenCV 2.x中所有主要部件的綁定：CxCORE (almost complete), CxFLANN (complete), Cv (complete),
CvAux (C++ part almost...

視覺快速開發平台 qcv

計算機視覺快速開發平台，提供測試框架，使開發者可以專注於算法研究。

圖像捕獲 libv4l2cam

對函數庫v412的封裝，從網絡攝像頭等硬件獲得圖像數據，支持YUYV裸數據輸出和BGR24的OpenCV  IplImage輸出

計算機視覺算法 OpenVIDIA

OpenVIDIA projects implement computer vision algorithms running on on graphics hardware such as single or multiple graphics processing units(GPUs) using
OpenGL, Cg and CUDA-C. Some samples will soon support OpenCL and Direct Compute API'...

高斯模型點集配准算法 gmmreg

實現了基於混合高斯模型的點集配准算法，該算法描述在論文： A Robust Algorithm for Point Set Registration Using Mixture of Gaussians, Bing Jian and Baba C. Vemuri. ，實現了C++/Matlab/Python接口...

模式識別和視覺庫 RAVL

Recognition And Vision Library (RAVL) 是一個通用 C++ 庫，包含計算機視覺、模式識別等模塊。

圖像處理和計算機視覺常用算法庫 LTI-Lib

LTI-Lib 是一個包含圖像處理和計算機視覺常用算法和數據結構的面向對象庫，提供 Windows 下的 VC 版本和 Linux 下的 gcc 版本，主要包含以下幾方面內容： 1、線性代數 2、聚類分析 3、圖像處理 4、可視化和繪圖工具

OpenCV優化 opencv-dsp-acceleration

優化了OpenCV庫在DSP上的速度。

C++計算機視覺庫 Integrating Vision Toolkit

Integrating Vision Toolkit (IVT) 是一個強大而迅速的C++計算機視覺庫，擁有易用的接口和面向對象的架構，並且含有自己的一套跨平台GUI組件，另外可以選擇集成OpenCV

計算機視覺和機器人技術的工具包 EGT

The Epipolar Geometry Toolbox (EGT) is a toolbox designed for Matlab (by Mathworks Inc.). EGT provides a wide set of functions to approach computer vision
and robotics problems with single and multiple views, and with different vision se...

OpenCV的擴展庫 ImageNets

ImageNets 是對OpenCV 的擴展，提供對機器人視覺算法方面友好的支持，使用Nokia的QT編寫界面。

libvideogfx

視頻處理、計算機視覺和計算機圖形學的快速開發庫。

Matlab計算機視覺包 mVision

Matlab 的計算機視覺包，包含用於觀察結果的 GUI 組件，貌似也停止開發了，拿來做學習用挺不錯的。

Scilab的計算機視覺庫 SIP

SIP 是 Scilab（一種免費的類Matlab編程環境）的圖像處理和計算機視覺庫。SIP 可以讀寫 JPEG/PNG/BMP 格式的圖片。具備圖像濾波、分割、邊緣檢測、形態學處理和形狀分析等功能。

STAIR Vision Library

STAIR Vision Library (SVL) 最初是為支持斯坦福智能機器人設計的，提供對計算機視覺、機器學習和概率統計模。

 

視覺相關網站

今天的主要任務就是和大家分享一些鄙人收藏的認為相當研究價值的網頁：

Oxford大牛：Andrew Zisserman，http://www.robots.ox.ac.uk/~vgg/hzbook/code/，此人主要研究多幅圖像的幾何學，該網站提供了部分工具，相當實用，還有例子

西澳大利亞大學的Peter Kovesi：http://www.csse.uwa.edu.au/~pk/research/matlabfns/，提供了一些基本的matlab工具，主要內容涉及Computer Vision, Image Processing

CMU：http://www.cs.cmu.edu/afs/cs/project/cil/ftp/html/vision.html,該網站是我的最愛，尤其后面這個地址http://www.cs.cmu.edu/afs/cs/project/cil/ftp/html/v-groups.html，在這里提供了世界各地機構、大學在Computer Vision所涉及各領域的研究情況，包括Image Processing, Machine Vision，我后來也是通過它連接到了很多國外的網站

Cambridge：http://mi.eng.cam.ac.uk/milab.html，這是劍橋大學的機器智能實驗室，里面有三個小組，Computer Vision & Robotics, Machine Intelligence, Speech，目前為止，Computer Vision & Robotics的一些研究成果對我日后的幫助可能會比較大，所以在此提及

大量計算機視覺方面的原版電子書：http://homepages.inf.ed.ac.uk/rbf/CVonline/books.htm，我今天先下了本Zisserman的書，呵呵，國外的原版書，雖然都是比較老的，但是對於基礎的理解學習還是很有幫助的，至於目前的研究現狀只能通過論文或者一些研究小組的網站

stanford：http://ai.stanford.edu/~asaxena/reconstruction3d/，這個網站是Andrew N.G老師和一個印度阿三的博士一起維護的，主要對於單張照片的三維重建，尤其他有個網頁make3d.stanford.edu可以讓你自己上傳你的照片，通過網站來重建三維模型，這個網站對於剛開始接觸Computer Vision的我來說，如獲至寶，但有個致命問題就是make3d已經無法注冊，我也多次給Andrew和印度阿三email，至今未回，郁悶，要是有這個網站的帳號，那還是相當爽的，不知道是不是由於他們的郵箱把我的email當成垃圾郵件過濾，哎，但這個stanford網站的貢獻主要是代碼，有很多computer vision的基礎工具，貌似40M左右，全都是基於matlab的

caltech：http://www.vision.caltech.edu/bouguetj/calib_doc/，這是我們Computer Vision老師課件上的連接，主要是用於攝像機標定的工具集，當然也有涉及對標定圖像三維重建的前期處理過程

JP Tarel：http://perso.lcpc.fr/tarel.jean-philippe/，這是他的個人主頁，也是目前為止我發的email中，唯一一個給我回信的老外，因為我需要重建練習的正是他的圖片集，我讀過他的論文，但沒有涉及代碼的內容，再加上又是94年以前的論文，很多相關的引文，我都無法下載，在我的再三追問下，Tarel教授只告訴我，你可以按照我的那篇論文對足球進行重建，可是...你知道嗎，你有很多圖像處理的引文都下不了了，我只知道你通過那篇文章做了圖像的預處理，根本不知道具體過程，當然我有幸找到過一篇90左右的論文，講的是region-based segmentation，可是這文章里所有引文又是找不到的....悲劇的人生

開源軟件網站：www.sourceforge.net

機器視覺開源代碼集合

一、特征提取Feature Extraction：

二、圖像分割Image Segmentation：

  • Normalized Cut [1] [Matlab code]
  • Gerg Mori’ Superpixel code [2] [Matlab code]
  • Efficient Graph-based Image Segmentation [3] [C++ code] [Matlab wrapper]
  • Mean-Shift Image Segmentation [4] [EDISON C++ code] [Matlab wrapper]
  • OWT-UCM Hierarchical Segmentation [5] [Resources]
  • Turbepixels [6] [Matlab code 32bit] [Matlab code 64bit] [Updated code]
  • Quick-Shift [7] [VLFeat]
  • SLIC Superpixels [8] [Project]
  • Segmentation by Minimum Code Length [9] [Project]
  • Biased Normalized Cut [10] [Project]
  • Segmentation Tree [11-12] [Project]
  • Entropy Rate Superpixel Segmentation [13] [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 [Project][other]

三、目標檢測Object Detection：

  • A simple object detector with boosting [Project]
  • INRIA Object Detection and Localization Toolkit [1] [Project]
  • Discriminatively Trained Deformable Part Models [2] [Project]
  • Cascade Object Detection with Deformable Part Models [3] [Project]
  • Poselet [4] [Project]
  • Implicit Shape Model [5] [Project]
  • Viola and Jones’s Face Detection [6] [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、顯著性檢測Saliency Detection：

  • Itti, Koch, and Niebur’ saliency detection [1] [Matlab code]
  • Frequency-tuned salient region detection [2] [Project]
  • Saliency detection using maximum symmetric surround [3] [Project]
  • Attention via Information Maximization [4] [Matlab code]
  • Context-aware saliency detection [5] [Matlab code]
  • Graph-based visual saliency [6] [Matlab code]
  • Saliency detection: A spectral residual approach. [7] [Matlab code]
  • Segmenting salient objects from images and videos. [8] [Matlab code]
  • Saliency Using Natural statistics. [9] [Matlab code]
  • Discriminant Saliency for Visual Recognition from Cluttered Scenes. [10] [Code]
  • Learning to Predict Where Humans Look [11] [Project]
  • Global Contrast based Salient Region Detection [12] [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、圖像分類、聚類Image Classification, Clustering

  • Pyramid Match [1] [Project]
  • Spatial Pyramid Matching [2] [Code]
  • Locality-constrained Linear Coding [3] [Project] [Matlab code]
  • Sparse Coding [4] [Project] [Matlab code]
  • Texture Classification [5] [Project]
  • Multiple Kernels for Image Classification [6] [Project]
  • Feature Combination [7] [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、摳圖Image Matting

  • A Closed Form Solution to Natural Image Matting [Code]
  • Spectral Matting [Project]
  • Learning-based Matting [Code]

七、目標跟蹤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：

九、3D相關：

  • 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、機器學習算法：

  • 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 (余凱)[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、目標、行為識別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[Project]
  • A Hough Transform-Based Voting Framework for Action Recognition[Project]
  • Motion Interchange Patterns for Action Recognition in Unconstrained Videos[Project]
  • 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、圖像處理：

  • Distance Transforms of Sampled Functions[Project]
  • The Computer Vision Homepage[Project]
  • Efficient appearance distances between windows[code]
  • Image Exploration algorithm[code]
  • Motion Magnification 運動放大 [Project]
  • Bilateral Filtering for Gray and Color Images 雙邊濾波器 [Project]
  • A Fast Approximation of the Bilateral Filter using a Signal Processing Approach [Project]

十三、一些實用工具：

  • 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、人手及指尖檢測與識別：

  • finger-detection-and-gesture-recognition [Code]
  • Hand and Finger Detection using JavaCV[Project]
  • Hand and fingers detection[Code]

十五、場景解釋：

  • 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、圖像檢索Image Retrieval：

  • Semi-Supervised Distance Metric Learning for Collaborative Image Retrieval [Paper][code]

十八、馬爾科夫隨機場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、運動檢測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 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 

  • 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 

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 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

Feature Detection and Description 

Action Recognition

RGBD Recognition 


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