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
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 support 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 v11)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?
implementation of YOLO11 with images and videos
training YOLO with custom dataset
Section 7 - Single Shot MultiBox Detector (SSD) Object Detection Algorithm SDD
what is the main idea behind SSDalgorithm
constructing anchor boxes
VGG16 and MobileNet architectures
implementing SSD with real-time videos
Section 8 - Object Tracking Algorithms
DeepSORTobject detection algorithm
ByteTrack algorithm
BoTSORT algorithm
implementation of object tracking
vehicle counting algorithm
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!
