简介计算机视觉是在图像处理的基础上发展起来的新兴学科，在计算机科学和工程、信号处理、物理学、应用数学和统计学，神经生理学和认知科学等研究方面，在制造业、检验、文档分析、医疗诊断，和军事等领域等各种智能／自主应用方面，都有非常广阔的发展前景。
由于涉及到如此多的专业知识，对普通的研发人员而言，计算机视觉颇有些阳春白雪的意味。其实这种意味来自于两个方面，即它是学术研究与工程开发的集合体。纯粹的研究人员，在有好的想法或者概念情况下，需要一个工程开发工具来验证自己的想法，这个开发工具必须是简单而易用的；工程人员则由于专业背景知识的缺乏，非常难以介入到计算机视觉领域。而 OpenCV 恰恰为这两者的结合提供了一个得心应手的开发工具或者应用平台。
OpenCV 作为一个开放源代码的应用平台，最大程度上体现出“众人拾柴火焰高”的开放精神。有大量的 OpenCV 学习资源可以在互联网上找到，这里译者深深感谢互联网的发展，一言以蔽之，没有互联网，就没有 OpenCV 。因此 OpenCV 发展到今天，已经快速从少数人的兴趣爱好逐步转变为一个系统的、有科研和商业应用价值的研发平台。
作为 OpenCV 项目的发起人， Gary Bradski 和 Adrain Kaebler 所撰写的《
Learning OpenCV 》一书，对 OpenCV 的很多基本算法函数都给出了详细的阐述，并且对函数算法的说明也非常到位。在阅读本书的过程中，读者不但有“知其然”，而且有“知其所以然”的感受。
本书在介绍计算机视觉各个算法思想的同时，通过大量的程序样例，给读者以启发和引导，始终体现出 “ 学以致用 ” 的精神。特别是每章之后的练习，让读者在浏览各章节内容的基础上，籍此做更进一步的思考，对读者在视觉算法思想的领悟和视野的拓展大有裨益。“桃李不言，下自成蹊”，对本书真实价值的最有效评判，其实是来自于广大的读者。
English desciptionLearning OpenCV puts you right in the middle of the rapidly expanding field of computer vision. Written by the creators of OpenCV, the widely used free open-source library, this book introduces you to computer vision and demonstrates how you can quickly build applications that enable computers to "see" and make decisions based on the data.
Computer vision is everywhere -- in security systems, manufacturing inspection systems, medical image analysis, Unmanned Aerial Vehicles, and more. It helps robot cars drive by themselves, stitches Google maps and Google Earth together, checks the pixels on your laptop's LCD screen, and makes sure the stitches in your shirt are OK.
OpenCV provides an easy-to-use computer vision infrastructure along with a comprehensive library containing more than 500 functions that can run vision code in real time. With Learning OpenCV, any developer or hobbyist can get up and running with the framework quickly, whether it's to build simple or sophisticated vision applications.
The book includes:
* A thorough introduction to OpenCV
* Getting input from cameras
* Transforming images
* Shape matching
* Pattern recognition, including face detection
* Segmenting images
* Tracking and motion in 2 and 3 dimensions
* Machine learning algorithms
Hands-on exercises at the end of each chapter help you absorb the concepts, and an appendix explains how to set up an OpenCV project in Visual Studio. OpenCV is written in performance optimized C/C++ code, runs on Windows, Linux, and Mac OS X, and is free for commercial and research use under a BSD license.
Getting machines to see is a challenging but entertaining goal. If you're intrigued by the possibilities, Learning OpenCV gets you started on building computer vision applications of your own.
封面附件:
cover.jpg 在此购买，立刻节省25%学习OpenCV(中文版)PDF及源码下载
-