opencv3.1发布模块介绍

本文主要是介绍opencv3.1发布模块介绍，希望对大家解决编程问题提供一定的参考价值，需要的开发者们随着小编来一起学习吧！

OpenCV 3.1 发布，此版本更新内容如下：此版本是 3.x 系列的第一个稳定版本，详细改进内容请看发行说明。

OpenCV 3.1 支持最新的 Windows 和 OS X 操作系统和最新的开发工具 (VS2015 和 Xcode 7)；支持 Andorid 5。

主要改进：

Omnidirectional Cameras Calibration and Stereo 3D Reconstruction全方位摄像机标定和立体三维重建 – opencv_contrib/ccalib module (Baisheng Lai, Bo Li)->
全方位摄像机标定和立体三维重建
Structure From Motion – opencv_contrib/sfm module (Edgar Riba, Vincent Rabaud)->从运动信息中恢复三维场景结构
Improved Deformable Part-based Models – opencv_contrib/dpm module (Jiaolong Xu, Bence Magyar)->改进的变形部件为基础的模型
Real-time Multi-object Tracking using Kernelized Correlation Filter – opencv_contrib/tracking module (Laksono Kurnianggoro, Fernando J. Iglesias Garcia)->采用核心化相关滤波实时多目标跟踪
Improved and expanded Scene Text Detection – opencv_contrib/text module (Lluis Gomez, Vadim Pisarevsky)->改进和扩展场景文字探测
Stereo correspondence improvements – opencv_contrib/stereo module (Mircea Paul Muresan, Sergei Nosov)->立体匹配的改进
Structured-Light System Calibration – opencv_contrib/structured_light (Roberta Ravanelli, Delia, Stefano Fabri, Claudia Rapuano)->结构光系统标定
Chessboard+ArUco for camera calibration – opencv_contrib/aruco (Sergio Garrido, Prasanna, Gary Bradski)->运用棋盘+ArUco库标定摄像头
Implementation of universal interface for deep neural network frameworks – opencv_contrib/dnn module (Vitaliy Lyudvichenko, Anatoly Baksheev)->深度神经网络框架的通用接口的实现
Recent advances in edge-aware filtering, improved SGBM stereo algorithm – opencv/calib3d and opencv_contrib/ximgproc (Alexander Bokov, Maksim Shabunin)->边缘意识过滤的最新进展，改进SGBM立体算法
Improved ICF detector, waldboost implementation – opencv_contrib/xobjdetect (Vlad Shakhuro, Alexander Bovyrin)->改进的ICF检测，waldboost实现
Multi-target TLD tracking – opencv_contrib/tracking module (Vladimir Tyan, Antonella Cascitelli)->多目标TLD算法跟踪
3D pose estimation using CNNs – opencv_contrib/cnn_3dobj (Yida Wang, Manuele Tamburrano, Stefano Fabri)->3D姿态估计使用细胞神经网络