主要包括有OpenCV、Weka和Matlab，另外其中包含LibSVM、Vlfeat和DeepLearnToolbox等。

1.OpenCV

主页：http://opencv.org/

下载地址：http://opencv.org/downloads.html

编程环境：VS

版本：3.0.0

教程：doc\opencv_tutorials.pdf

API接口：doc\opencv2refman.pdf

特征检测：

• “FAST” – FastFeatureDetector

• “STAR” – StarFeatureDetector

• “SIFT” – SIFT (nonfree module)

• “SURF” – SURF (nonfree module)

• “ORB” – ORB

• “BRISK” – BRISK

• “MSER” – MSER

• “GFTT” – GoodFeaturesToTrackDetector

• “HARRIS” – GoodFeaturesToTrackDetector with Harris detector enabled

• “Dense” – DenseFeatureDetector

• “SimpleBlob” – SimpleBlobDetector

描述符提取：

• “SIFT” – SIFT

• “SURF” – SURF

• “BRIEF” – BriefDescriptorExtractor

• “BRISK” – BRISK

• “ORB” – ORB

• “FREAK” – FREAK

机器学习：

Normal Bayes Classifier

K-Nearest Neighbors

Support Vector Machines

Decision Trees

Boosting

Gradient Boosted Trees

Random Trees

Extremely randomized trees

Expectation Maximization

Neural Networks

kmeans

2.Weka

下载地址：http://www.cs.waikato.ac.nz/ml/weka/downloading.html

LibSVM下载地址：http://www.csie.ntu.edu.tw/~cjlin/libsvm/

编程环境：Eclipse

版本：3.6

教程：doc\index.html

机器学习：

1)weka.classifiers.bayes

AODE

AODEsr

BayesianLogisticRegression

BayesNet

ComplementNaiveBayes

DMNBtext

HNB

NaiveBayes

NaiveBayesMultinomial

NaiveBayesMultinomialUpdateable

NaiveBayesSimple

NaiveBayesUpdateable

WAODE

2)weka.classifiers.functions

GaussianProcesses

IsotonicRegression

LeastMedSq

LibLINEAR

LibSVM

LinearRegression

Logistic

MultilayerPerceptron

PaceRegression

PLSClassifier

RBFNetwork

SimpleLinearRegression

SimpleLogistic

SMO

SMOreg

SPegasos

VotedPerceptron

Winnow

3)weka.classifiers.lazy

IB1

IBk

KStar

LBR

LWL

4)weka.classifiers.meta

AdaBoostM1

AdditiveRegression

AttributeSelectedClassifier

Bagging

ClassificationViaClustering

ClassificationViaRegression

CostSensitiveClassifier

CVParameterSelection

Dagging

Decorate

END

FilteredClassifier

Grading

GridSearch

LogitBoost

MetaCost

MultiBoostAB

MultiClassClassifier

MultiScheme

OrdinalClassClassifier

RacedIncrementalLogitBoost

RandomCommittee

RandomSubSpace

RegressionByDiscretization

RotationForest

Stacking

StackingC

ThresholdSelector

Vote

5)weka.classifiers.mi

CitationKNN

MDD

MIBoost

MIDD

MIEMDD

MILR

MINND

MIOptimalBall

MISMO

MISVM

MIWrapper

SimpleMI

6)weka.classifiers.rules

ConjunctiveRule

DecisionTable

DecisionTableHashKey

DTNB

JRip

M5Rules

NNge

OneR

PART

Prism

Ridor

Rule

RuleStats

ZeroR

7)weka.classifiers.trees

ADTree

BFTree

DecisionStump

FT

Id3

J48

J48graft

LADTree

LMT

M5P

NBTree

RandomForest

RandomTree

REPTree

SimpleCart

UserClassifier

8)weka.clusterers

AbstractClusterer

AbstractDensityBasedClusterer

CheckClusterer

CLOPE

ClusterEvaluation

Cobweb

DBSCAN

EM

FarthestFirst

FilteredClusterer

HierarchicalClusterer

MakeDensityBasedClusterer

OPTICS

RandomizableClusterer

RandomizableDensityBasedClusterer

RandomizableSingleClustererEnhancer

sIB

SimpleKMeans

SingleClustererEnhancer

XMeans

3.Matlab

1)DeepLearnToolbox工具箱

下载地址：https://github.com/yangzhixuan/DeepLearnToolbox

教程：看包含的示例程序

CNN

SAE

DBN

CAE

NN

2)Vlfeat工具箱

下载地址：http://www.vlfeat.org/download.html

视觉特征：

HOG

SIFT

DSIFT

LIOP

MSER

机器学习：

GMM

K-means

AIB

Quick shift

SLIC

SVM

Forests of kd-trees

3)Neural Network Toolbox工具箱

所有关于神经网络的开发

4)其他基本工具箱

特征检测：

BRISK

FAST

Harris–Stephens

minimum eigenvalue

MSER

SURF

描述符提取：

extractFeatures

HOG

分类/回归：

Linear Regression

Nonlinear Regression

Generalized Linear Models

Classification Trees and Regression Trees

Discriminant Analysis

Naive Bayes Classification

Nearest Neighbors

Model Building and Assessment

聚类：

Hierarchical Clustering

k-Means Clustering

Gaussian Mixture Models

Hidden Markov Models

集成： Boosting Bagging Random Subspace