Computer Vision Bootcamp with Python (OpenCV)

Viola-Jones method, HOG features, R-CNNs, YOLO and SSD (Single Shot) Object Detection Approaches with Python and OpenCV

What you’ll learn:
Have a good understanding of the most powerful Computer Vision models
Understand OpenCV
Understand and implement Viola-Jones algorithm
Understand and implement Histogram of Oriented Gradients (HOG) algorithm
Understand and implement convolutional neural network (CNN) related computer vision approaches
Understand and implement YOLO (You Only Look Once) algorithm
Single Shot MultiBox Detection SDD algorithm
Master face detection and object detection

Requirements
Basic Python programming skills

Description
This course is about the fundamental concept of image processing, focusing on face detection and object detection. These topics are getting very hot nowadays because these learning algorithms can be used in several fields from software engineering to crime investigation. Self-driving cars (for example lane detection approaches) relies heavily on computer vision.

With the advent of deep learning and graphical processing units (GPUs) in the past decade it’s become possible to run these algorithms even in real-time videos. So what are you going to learn in this course?

Section 1 – Image Processing Fundamentals:

computer vision theory

what are pixel intensity values

convolution and kernels (filters)

blur kernel

sharpen kernel

edge detection in computer vision (edge detection kernel)

Section 2 – Serf-Driving Cars and Lane Detection

how to use computer vision approaches in lane detection

Canny’s algorithm

how to use Hough transform to find lines based on pixel intensities

Section 3 – Face Detection with Viola-Jones Algorithm:

Viola-Jones approach in computer vision

what is sliding-windows approach

detecting faces in images and in videos

Section 4 – Histogram of Oriented Gradients (HOG) Algorithm

how to outperform Viola-Jones algorithm with better approaches

how to detects gradients and edges in an image

constructing histograms of oriented gradients

using suppor vector machines (SVMs) as underlying machine learning algorithms

Section 5 – Convolution Neural Networks (CNNs) Based Approaches

what is the problem with sliding-windows approach

region proposals and selective search algorithms

region based convolutional neural networks (C-RNNs)

fast C-RNNs

faster C-RNNs

Section 6 – You Only Look Once (YOLO) Object Detection Algorithm

what is the YOLO approach?

constructing bounding boxes

how to detect objects in an image with a single look?

intersection of union (IOU) algorithm

how to keep the most relevant bounding box with non-max suppression?

Section 7 – Single Shot MultiBox Detector (SSD) Object Detection Algorithm SDD

what is the main idea behind SSD algorithm

constructing anchor boxes

VGG16 and MobileNet architectures

implementing SSD with real-time videos

We will talk about the theoretical background of face recognition algorithms and object detection in the main then we are going to implement these problems on a step-by-step basis.

Thanks for joining the course, let’s get started!

Who this course is for

Anyone interested in machine learning (artificial intelligence) and computer vision

Computer Vision Bootcamp™ with Python (OpenCV) - YOLO, SSD