Section 1 : Course Introduction and Setup

lecture 1 Introduction 1:57
lecture 2 Introduction to Computer Vision and OpenCV 3:9
lecture 3 About this course 5:14
lecture 4 READ THIS - Guide to installing and setting up you Text
lecture 5 Recomended - Setup your OpenCV4.0.1 Virtual Machin 5:42
lecture 6 Windows+OpenCV+Installation Pdf
lecture 7 Installation of OpenCV & Python on Mac Text
lecture 8 Installation of OpenCV & Python on Linux Text
lecture 9 Set up course materials (DOWNLOAD LINK BELOW) - No 1:42

Section 2 : Basics of Computer Vision and OpenCV

lecture 10 What are Images 2:27
lecture 11 How are Images Formed 3:20
lecture 12 Storing Images on Computers 5:24
lecture 13 Getting Started with OpenCV - A Brief OpenCV Intro 9:20
lecture 14 Grayscaling - Converting Color Images To Shades of 2:0
lecture 15 Understanding Color Spaces - The Many Ways Color I 12:13
lecture 16 Histogram representation of Images - Visualizing t 4:38
lecture 17 Creating Images & Drawing on Images - Make Squares 3:47

Section 3 : Image Manipulations & Processing

lecture 18 Transformations, Affine And Non-Affine - The Many 2:22
lecture 19 Image Translations - Moving Images Up, Down. Left 2:47
lecture 20 Rotations - How To Spin Your Image Around And Do H 3:11
lecture 21 Scaling, Re-sizing and Interpolations - Understand 4:27
lecture 22 Image Pyramids - Another Way of Re-Sizing 1:53
lecture 23 Cropping - Cut Out The Image The Regions You Want 2:42
lecture 24 Arithmetic Operations - Brightening and Darkening 3:37
lecture 25 Bitwise Operations - How Image Masking Works 3:36
lecture 26 Blurring - The Many Ways We Can Blur Images & Why 7:29
lecture 27 Sharpening - Reverse Your Images Blurs 1:51
lecture 28 Thresholding (Binarization) - Making Certain Image 8:39
lecture 29 Dilation, Erosion, OpeningClosing - Importance of 4:58
lecture 30 Edge Detection using Image Gradients & Canny Edge 4:52
lecture 31 Perspective & Affine Transforms - Take An Off Ang 3:56
lecture 32 Mini Project 1 - Live Sketch App - Turn your Webca 5:3

Section 4 : Image Segmentation & Contours

lecture 33 Segmentation and Contours - Extract Defined Shapes 11:11
lecture 34 Sorting Contours - Sort Those Shapes By Size 13:0
lecture 35 Approximating Contours & Finding Their Convex Hull 5:42
lecture 36 Matching Contour Shapes - Match Shapes In Images E 5:28
lecture 37 Mini Project 2 - Identify Shapes (Square, Rectangl 5:30
lecture 38 Line Detection - Detect Straight Lines E.g. The Li 6:24
lecture 39 Circle Detection Text
lecture 40 Blob Detection - Detect The Center of Flowers 3:20
lecture 41 Mini Project 3 - Counting Circles and Ellipses 6:6

Section 5 : Object Detection in OpenCV

lecture 42 Object Detection Overview 3:20
lecture 43 Mini Project # 4 - Finding Waldo (Quickly Find A S 2:46
lecture 44 Feature Description Theory - How We Digitally Repr 4:37
lecture 45 Finding Corners - Why Corners In Images Are Import 6:46
lecture 46 SIFT, SURF, FAST, BRIEF & ORB - Learn The Differen 10:16
lecture 47 Mini Project 5 - Object Detection - Detect A Speci 14:58
lecture 48 Histogram of Oriented Gradients - Another Novel Wa 8:10

Section 6 : Object Detection - Build a Face, People and CarVehicle Detec

lecture 49 HAAR Cascade Classifiers - Learn How Classifiers W 5:12
lecture 50 Face and Eye Detection - Detect Human Faces and Ey 10:40
lecture 51 Mini Project 6 - Car and Pedestrian Detection in V 6:46

Section 7 : Augmented Reality (AR) - Facial Landmark Identification (Fac

lecture 52 Face Analysis and Filtering - Identify Face Outlin 10:57
lecture 53 Merging Faces (Face Swaps) - Combine Two Faces For 9:27
lecture 54 Mini Project 7 - Live Face Swapper (like MSQRD & S
lecture 55 Mini Project 8 - Yawn Detector and Counter 8:45

Section 8 : Simple Machine Learning using OpenCV

lecture 56 Machine Learning Overview - What Is It & Why It's 8:54
lecture 57 Mini Project 9 - Handwritten Digit Classification 20:0
lecture 58 Mini Project # 10 - Facial Recognition - Make Your 12:7

Section 9 : Object Tracking & Motion Analysis

lecture 59 Filtering by Color 6:15
lecture 60 Background Subtraction and Foreground Subtraction 6:55
lecture 61 Using Meanshift for Object Tracking 4:56
lecture 62 Using CAMshift for Object Tracking 4:4
lecture 63 Optical Flow - Track Moving Objects In Videos 7:18
lecture 64 Mini Project # 11 - Ball Tracking 5:2

Section 10 : Computational Photography & Make a License Plate Reader

lecture 65 Mini Project # 12 - Photo-Restoration 6:34
lecture 66 Mini Project # 13 - Automatic Number-Plate Recogn Text

Section 11 : Conclusion

lecture 67 Course Summary and how to become an Expert 2:51
lecture 68 Latest Advances, 12 Startup Ideas & Implementing C 7:6

Section 12 : BONUS - Deep Learning Computer Vision 1 - Setup a Deep Learn

lecture 69 Setup your Deep Learning Virtual Machine 10:28
lecture 70 Intro to Handwritten Digit Classification (MNIST) 5:47
lecture 71 Intro to Multiple Image Classification (CIFAR10) 2:52

Section 13 : BONUS - Deep Learning Computer Vision 2 - Introduction to Ne

lecture 72 Neural Networks Chapter Overview 1:35
lecture 73 Machine Learning Overview 8:26
lecture 74 Neural Networks Explained 3:51
lecture 75 Forward Propagation 8:34
lecture 76 Activation Functions 8:31
lecture 77 Training Part 1 – Loss Functions 9:13
lecture 78 Training Part 2 – Backpropagation and Gradient Des 9:57
lecture 79 Backpropagation & Learning Rates – A Worked Exampl 13:36
lecture 80 Regularization, Overfitting, Generalization and Te 15:25
lecture 81 Epochs, Iterations and Batch Sizes 3:38
lecture 82 Measuring Performance and the Confusion Matrix 7:7
lecture 83 Review and Best Practices 4:16

Section 14 : BONUS - Deep Learning Computer Vision 3 - Convolutional Neur

lecture 84 Convolutional Neural Networks Chapter Overview 1:0
lecture 85 Introduction to Convolutional Neural Networks (CNN 5:24
lecture 86 Convolutions & Image Features 13:20
lecture 87 Depth, Stride and Padding 6:51
lecture 88 ReLU 1:48
lecture 89 Pooling 4:37
lecture 90 The Fully Connected Laye
lecture 91 Training CNNs 3:8
lecture 92 Designing Your Own CNN 3:48

Section 15 : BONUS - Deep Learning Computer Vision 4 - Build CNNs in Pyth

lecture 93 Introduction to Keras & Tensorflow
lecture 94 Building a CNN in Keras 12:15
lecture 95 Building a Handwriting Recognition CNN 1:48
lecture 96 Loading Our Data 5:42
lecture 97 Getting our data in ‘Shape’ 4:4
lecture 98 Hot One Encoding 2:55
lecture 99 Building & Compiling Our Model 3:45
lecture 100 Training Our Classifier 4:58
lecture 101 Plotting Loss and Accuracy Charts 2:52
lecture 102 Saving and Loading Your Model 2:52
lecture 103 Displaying Your Model Visually 2:43
lecture 104 Building a Simple Image Classifier using CIFAR10 7:20

Section 16 : BONUS - Deep Learning Computer Vision 5 - Build a Cats vs Do

lecture 105 Data Augmentation Chapter Overview 1:0
lecture 106 Splitting Data into Test and Training Datasets 10:13
lecture 107 Train a Cats vs. Dogs Classifier
lecture 108 Boosting Accuracy with Data Augmentation 5:13
lecture 109 Types of Data Augmentation

Section 17 : BONUS - Build a Credit Card Number Reader

lecture 110 Step 1 - Creating a Credit Card Number Datase Text
lecture 111 Step 2 - Training Our Model Text
lecture 112 Step 3 - Extracting A Credit Card from the Backgro Text
lecture 113 Step 4 - Use our Model to Identify the Digits & D Text

Section 18 : BONUS - Neural Style Transfer with OpenCV

lecture 114 Perform Neural Style Transfer Using OpenCV4 Text

Section 19 : BONUS - Object Detection - Use SSDs (Single Shot Detector) f

lecture 115 Using an SSD In OpenCV Text

Section 20 : BONUS - Colorize Black and White Images

lecture 116 Colorizing Black and White Images Using Caffe Text