课程目录：
课程简介
本课程课程大纲从图像处理基础、机器视觉中的特征提取与描述、坐标变换与视觉测量，到数据处理、图像搜索、深度学习在图像识别中的应用、图像标注与问答、3D计算机视觉、机器视觉项目实战。整个课程由浅入深，结合案例真枪实战，是不可多得的CV上佳课程。) K0 ]7 ^5 wE# @. c) n$ |; N
课后视频：一周两课，每周六/日上午上课。% n* E0 z6 A/ |- m+ Y% _
课程语言：OpenCV（C/C++编写） + Matlab + Python’ d2 K0 V) N. V+ O) m0 x/ {9 S4 x
所需基础：一定的编程、图像处理基础，或者说学过编程 学过点图像处理。+ x9 F" V’ z$ }0 y2 [, d4 H
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应用实战：从CV基础，到深度学习实战，由浅入深，每次课都推出实例应用，注重深入浅出、真枪实战。
《计算机视觉班 》课程大纲
金
第一讲 工欲善其事必先利其器——图像处理基础
1. CV背景介绍
2. OpenCV完全解析基础
3. 图像的基本操作：遍历图像6种方法，ROI选取等
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第二讲初探计算机视觉2 ^2 u: ^, W6 V& }1 W
1. VideoCapture类介绍
2. Opencv编程常见错误, u9 U$ x- c+ e3 i" w. g! t
3. Python环境搭建+语法0 F" @, \’ DB
应用：机器学习在CV中的应用（python与C++多种演示）
第三讲 空域图像处理的洪荒之力/ \. A: ~% Tl
1. 图像卷积运算; ]* c2 E- T8 J/ R/ `
2. 边缘检测方法：sobel，canny以及图像拉普拉斯& J% m9 Z! v( Y8 G$ ?5 n
应用：车牌识别项目6 D) T* @" e- z6 @% J$ J
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第四讲：机器视觉中的特征提取与描述
1. 霍夫变换5 y. k; Vy; v& ]2 @0 T
2. 局部特征大汇总（GFTT，SIFT，SURF，FAST，ORB…）7 m2 Mh/ C4 Z/ i7 p
应用：无人车项目提示
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第五讲：坐标变换与视觉测量
1. 相机模型
2. 2D、3D、坐标变换
3. 相机标定
应用：增强现实技术simple VR不神秘（第四，第五讲综合演练）: d) q" \2 T/ TE$ I
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第六讲：深度学习在图像识别中的应用
分类：linear regression, neural networks
检测：bounding box regression/ X2 `" K2 g; J/ F8 q
定位：localization+ O3 n3 N# N# X" N& j7 `8 y
应用：使用CNN进行图像识别0 X! `4 J" D9 r9 c: W: o8 m
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第七讲：图像检索
1. 图像检索与特征抽取- [: u’ x# U: S& k9 x9 [0 V
2. 海量数据与快速检索
3. 电商商品检索技术
应用：基于Tensorflow与近似最近邻查找的图像检索示例
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第八讲：图像标注与问答2 v: i* f- A( }; U’ t7 r?1 G
1. 语言模型介绍
2. LSTM模型与标注问题% V+ Y/ i) ]$ T
3. 应用：DenseCaption in Generating Captions in Images.! [+ K6 d/ ]" M
第九讲：3D计算机视觉/ A+ D) nQ* L* X
1. 表面和外形重构
2. 基于模型的重构
3. 应用：人脸动画
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第十讲：机器视觉项目实战
CV 实战：以鲸鱼识别为例，利用深度学习解决Kaggle竞赛中的图像分类问题
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