The goal of this course is design an application that can determine the type of the traffic sign that is displayed in an image, and is robust to different real-life conditions such as poor lighting, obstructions or the sign being far away.
Part 1: Concepts, Basic Operations
What is Computer Vision?
Installation of Python 3 in Windows
Installation of OpenCV & Other Packages for Image Processing
Setting up the Working Directory for OpenCV
Read an Image using OpenCV
Save Image using waitKey Function in OpenCV
Computer Vision - Basic Concepts of an Image
Part 2 : Basic Image Operations
Basic Image Operations
Color Spaces in Image Processing
Properties & Pixels of an Image - Basic Image Operations
Draw Geometric Shapes using OpenCV
Change Color Space of an Image in OpenCV
Basic Image Operations - Splitting Image Channels
Plot an Image in Python using matplotlib
Plot Multiple Images in Python using matplotlib
Region of Interest in an Image - Basic Image Operations using OpenCV
Part 3: Feature Engineering
How to calculate Histogram of Oriented Gradients?
Compute the HOG descriptor by using scikit-image library
Part 4: Sliding windowns, Non-Maximum Suppression
What is a sliding window?
Sliding Windows for Object Detection
Utilizing non-maximum suppression to ignore redundant, overlapping bounding boxes.
Part 5: Selective Search
How Image Segmentation Works
How Selective Search Works
How to use Selective Search based segmentation implemented in OpenCV
Part 6: Support Vector Machine
What is a SVM?
How is the optimal hyperplane computed?
Set up the training data
Set up SVM's parameters
Train the SVM
Regions classified by the SVM
Part 7: Traffic sign recognition
Traffic sign recognition algorithm
Train SVM Traffic sign model
Region Proposal by Selective Search
Classification of traffic signs based on the svm model and proposed regions
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