cv相關博客文章

　　收藏些圖像處理，機器學習，深度學習方面比較不錯的文章，時常學習，複習和膜拜吧。。。

圖像方面（傳統CV）：

1. SIFT特徵

http://www.javashuo.com/article/p-tsfbxcyw-bu.html

http://shartoo.github.io/SIFT-feature/?FbmNv=5d9f3d0c8ca5090a

http://www.javashuo.com/article/p-vbbvzyua-p.html

2. HOG特徵

http://shartoo.github.io/HOG-feature/?FbmNv=5d9f3d48e0647071

https://senitco.github.io/2017/06/10/image-feature-hog/

http://www.javashuo.com/article/p-cqhorrko-cg.html

https://zhuanlan.zhihu.com/p/40960756

3. 圖像金字塔

http://shartoo.github.io/image-pramid/?FbmNv=5d9f3d6e990e41bb

https://zhuanlan.zhihu.com/p/80362140?utm_source=wechat_session&utm_medium=social&utm_oi=71873182302208

https://zhuanlan.zhihu.com/p/32815143

https://opencv-python-tutroals.readthedocs.io/en/latest/py_tutorials/py_imgproc/py_pyramids/py_pyramids.html

4. Haar特徵

http://shartoo.github.io/img-haar-feature/

https://senitco.github.io/2017/06/15/image-feature-haar/

https://juejin.im/post/5b0e6f83f265da0910791a38

http://www.javashuo.com/article/p-ztcyonsl-nd.html

 5.Harris角點

 https://www.cnblogs.com/ronny/p/4009425.html

https://senitco.github.io/2017/06/18/image-feature-harris/

https://zhuanlan.zhihu.com/p/42490675

https://zhuanlan.zhihu.com/p/36382429

機器學習方面：

1. Linear Regression

https://yoyoyohamapi.gitbooks.io/mit-ml/content/%E7%BA%BF%E6%80%A7%E5%9B%9E%E5%BD%92/articles/%E7%BA%BF%E6%80%A7%E5%9B%9E%E5%BD%92%E4%B8%8E%E6%A2%AF%E5%BA%A6%E4%B8%8B%E9%99%8D.html

https://zhuanlan.zhihu.com/p/45023349

2. Logistic Regression

https://chenrudan.github.io/blog/2016/01/09/logisticregression.html

https://www.jiqizhixin.com/articles/2018-05-13-3

https://zhuanlan.zhihu.com/p/28408516

3.Neutral Network

https://clyyuanzi.gitbooks.io/julymlnotes/content/dl_nn.html

http://www.javashuo.com/article/p-gwixiqzw-gw.html

神經網絡損失函數（loss function）:

 

4. 迴歸和正則化(Regression and Regularization)

https://www.zhihu.com/question/20924039

http://studyai.site/2016/09/04/%E6%96%AF%E5%9D%A6%E7%A6%8F%E6%9C%BA%E5%99%A8%E5%AD%A6%E4%B9%A0%E8%AF%BE%E7%A8%8B%20%E7%AC%AC%E4%B8%89%E5%91%A8%20(4)%E6%AD%A3%E5%88%99%E5%8C%96%EF%BC%9A%E8%A7%A3%E5%86%B3%E8%BF%87%E6%8B%9F%E5%90%88%E9%97%AE%E9%A2%98/

https://zhuanlan.zhihu.com/p/29957294

線性迴歸，邏輯迴歸和神經網絡帶正則化的損失函數：

 

 正則化項能減緩梯度的變化：

 

 

 5. SVM(support vector machine)

拉格朗日乘子法

https://www.zhihu.com/question/38586401/answer/457058079?utm_source=wechat_session&utm_medium=social&utm_oi=71873182302208&hb_wx_block=0

對偶問題：

https://zhuanlan.zhihu.com/p/31131842?utm_source=wechat_session&utm_medium=social&utm_oi=71873182302208

KKT條件：

https://zhuanlan.zhihu.com/p/26514613?utm_source=wechat_session&utm_medium=social&utm_oi=71873182302208

SVM原理：

https://webcache.googleusercontent.com/search?q=cache:0bibeWe0EQYJ:https://raw.githubusercontent.com/liuzheng712/Intro2SVM/master/Intro2SVM.pdf+&cd=13&hl=zh-CN&ct=clnk&gl=us

https://zhuanlan.zhihu.com/p/24638007?utm_source=wechat_session&utm_medium=social&utm_oi=71873182302208

https://www.jiqizhixin.com/articles/2018-10-17-20

https://www.cnblogs.com/leftnoteasy/archive/2011/05/02/basic-of-svm.html

https://wizardforcel.gitbooks.io/dm-algo-top10/content/svm-1.html

http://www.javashuo.com/article/p-fctewcoy-bo.html

支持向量機的表達式，拉格朗日函數，對偶問題和KKT條件：

軟間隔支持向量機的表達式，拉格朗日函數，對偶問題和KKT條件：

支持向量機非線性化的核函數：

SVM使用代碼（sklearn包）：（線性svm，和採用核函數的非線性SVM）

https://zhuanlan.zhihu.com/p/37640777?utm_source=wechat_session&utm_medium=social&utm_oi=71873182302208

 SVM的python實現： https://blog.csdn.net/laobai1015/article/details/82763033

 

6. kmeans算法

https://yoyoyohamapi.gitbooks.io/mit-ml/content/KMeans/articles/K-Means%E7%AE%97%E6%B3%95%E6%AD%A5%E9%AA%A4.html

https://www.csuldw.com/2015/06/03/2015-06-03-ml-algorithm-K-means/

https://bainingchao.github.io/2018/09/19/%E4%B8%80%E6%AD%A5%E6%AD%A5%E6%95%99%E4%BD%A0%E8%BD%BB%E6%9D%BE%E5%AD%A6K-means%E8%81%9A%E7%B1%BB%E7%AE%97%E6%B3%95/

http://www.javashuo.com/article/p-wunigchf-cs.html

 k-Means++

https://zhuanlan.zhihu.com/p/32375430

kmeans和kmeans++ python代碼實現：

https://github.com/silence-cho/cv-learning/blob/master/week4/assignment.py 

https://github.com/ViperBling/CV_Course/blob/master/Week5/K-Means%2B%2B/K-Means.py

 

7.KNN（k近鄰）算法

https://coolshell.cn/articles/8052.html

http://www.javashuo.com/article/p-ddgnkfbs-db.html

8.決策樹 (Decision tree)

https://bainingchao.github.io/2018/09/19/%E4%B8%80%E6%AD%A5%E6%AD%A5%E6%95%99%E4%BD%A0%E8%BD%BB%E6%9D%BE%E5%AD%A6%E5%86%B3%E7%AD%96%E6%A0%91%E7%AE%97%E6%B3%95/

https://www.csuldw.com/2015/05/08/2015-05-08-decision%20tree/

https://lotabout.me/2018/decision-tree/

https://blog.csdn.net/xbinworld/article/details/44660339

信息增益：

信息增益率：

基尼指數：

 

 

 

 ID3（信息增益）和C4.5（信息增益率）：https://zhuanlan.zhihu.com/p/26760551?utm_source=wechat_session&utm_medium=social&utm_oi=71873182302208

基尼不純度(基尼指數)：https://www.zhihu.com/question/296781126/answer/508112100?utm_source=wechat_session&utm_medium=social&utm_oi=71873182302208&hb_wx_block=0

sklearn實現決策樹：https://www.v2ex.com/amp/t/544062

 

 9.其餘算法

AdaBoost：

　　http://www.javashuo.com/article/p-aqhxqbdj-dn.html

　　http://www.javashuo.com/article/p-ubtcqosw-mv.html

LDA(隱式狄利克雷分佈):  https://github.com/endymecy/spark-ml-source-analysis/blob/master/%E8%81%9A%E7%B1%BB/LDA/lda.md

樸素貝葉斯：https://www.cnblogs.com/leoo2sk/archive/2010/09/17/naive-bayesian-classifier.html

                    https://zhuanlan.zhihu.com/p/26262151

　　　　　  python實現：https://zhuanlan.zhihu.com/p/32183117?utm_source=wechat_session&utm_medium=social&utm_oi=71873182302208

深度學習方面

1. overfit/underfit (過擬合和欠擬合)

https://marian5211.github.io/2018/03/08/%E3%80%90%E6%9C%BA%E5%99%A8%E5%AD%A6%E4%B9%A0%E3%80%91%E8%BF%87%E6%8B%9F%E5%90%88%E3%80%81%E6%AC%A0%E6%8B%9F%E5%90%88%E5%8F%8A%E5%85%B6%E8%A7%A3%E5%86%B3%E5%8A%9E%E6%B3%95/

https://zh.d2l.ai/chapter_deep-learning-basics/underfit-overfit.html

https://zhuanlan.zhihu.com/p/29707029

 2. bias and variance (高誤差和高方差)

https://www.jianshu.com/p/a585d5506b1e

http://www.javashuo.com/article/p-brlexfzr-ec.html

http://nanshu.wang/post/2015-05-17/

http://www.voidcn.com/article/p-tqoebcaa-dq.html

3.卷積

 

 

 

 

 反捲積(Deconv / Transposed Convolution / Fractionally strided conv):

https://www.zhihu.com/question/48279880?sort=created

https://www.zhihu.com/question/48279880/answer/838063090

 

4. Gradient vanishing and explosion （梯度消失和梯度爆炸）

http://www.javashuo.com/article/p-oyrovvyw-nm.html

https://jimmy-walker.gitbooks.io/tensorflow/%E6%A2%AF%E5%BA%A6%E6%B6%88%E5%A4%B1%E5%92%8C%E6%A2%AF%E5%BA%A6%E7%88%86%E7%82%B8.html

https://hunto.github.io/%E6%9C%BA%E5%99%A8%E5%AD%A6%E4%B9%A0/2018/07/17/%E6%9C%BA%E5%99%A8%E5%AD%A6%E4%B9%A0%E4%B8%AD%E6%A2%AF%E5%BA%A6%E6%B6%88%E5%A4%B1%E4%B8%8E%E6%A2%AF%E5%BA%A6%E7%88%86%E7%82%B8%E9%97%AE%E9%A2%98%E8%AF%A6%E8%A7%A3.html

https://codertw.com/%E7%A8%8B%E5%BC%8F%E8%AA%9E%E8%A8%80/583004/

https://zhuanlan.zhihu.com/p/51490163

5.Backward（反向傳播）

https://juejin.im/entry/5ac056dc6fb9a028de44d620

https://tigerneil.gitbooks.io/neural-networks-and-deep-learning-zh/content/chapter2.html

https://github.com/INTERMT/BP-Algorithm

https://jdhao.github.io/2016/01/19/back-propagation-in-mlp-explained/

 

 

 

圖像分割模型：

1. FCN

https://zhuanlan.zhihu.com/p/62839949

http://iblue.tech/2019/05/11/%E8%AE%BA%E6%96%87%E7%AC%94%E8%AE%B0-FCN-%E7%94%A8%E4%BA%8E%E5%9B%BE%E5%83%8F%E5%88%86%E5%89%B2%E7%9A%84%E5%85%A8%E5%8D%B7%E7%A7%AF%E7%BD%91%E7%BB%9C/

https://zh.gluon.ai/chapter_computer-vision/fcn.html

 

 

 2.U-Net (E-Net)

http://www.javashuo.com/article/p-obrtkobb-mu.html

https://juejin.im/post/5d63eb7bf265da03e05b2065

https://zhuanlan.zhihu.com/p/31428783

http://tanqingbo.com/2018/10/08/%E8%AE%BA%E6%96%87%E7%AC%94%E8%AE%B0%EF%BC%9A%E7%94%A8%E4%BA%8E%E5%8C%BB%E5%AD%A6%E5%9B%BE%E5%83%8F%E5%88%86%E5%89%B2%E7%9A%84%E5%8D%B7%E7%A7%AF%E7%BD%91%E7%BB%9C/

https://zhuanlan.zhihu.com/p/57530767

 

3. E-Net

https://zhuanlan.zhihu.com/p/39430439

http://hellodfan.com/2018/01/02/%E8%AF%AD%E4%B9%89%E5%88%86%E5%89%B2%E8%AE%BA%E6%96%87-ENet/

https://zhuanlan.zhihu.com/p/31379024

 

 

4. Mask-RCNN

https://zhuanlan.zhihu.com/p/37998710

https://www.analyticsvidhya.com/blog/2019/07/computer-vision-implementing-mask-r-cnn-image-segmentation/

https://zhuanlan.zhihu.com/p/40538057

 

 

 

Image Style Transfer（圖像風格轉變）：

Perceptual Loss： Perceptual Losses for Real-Time Style Transferand Super-Resolution

Feature mimicking：  Mimicking Very Efficient Network for Object Detection

Model distillation： Distilling the Knowledge in a Neural Network

 

Image Enhancement (圖像加強)：

Learning a Deep Single Image Contrast Enhancerfrom Multi-Exposure Images

A Generic Deep Architecture for Single Image Reflection Removaland Image Smoothing  （反射移除）

 

深度學習框架

caffe教程：

https://blog.csdn.net/m0_38116269/article/details/88119001

https://zhuanlan.zhihu.com/p/24110318

https://absentm.github.io/2016/05/14/%E6%B7%B1%E5%BA%A6%E5%AD%A6%E4%B9%A0Caffe%E7%B3%BB%E5%88%97%E6%95%99%E7%A8%8B%E9%9B%86%E5%90%88/