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Computer Vision Masterclass in Python

person icon Holczer Balazs

4

Computer Vision Masterclass in Python

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

updated on icon Updated on Jul, 2024

language icon Language - English

person icon Holczer Balazs

English [CC]

category icon Data Science,Python

Lectures -75

Duration -6.5 hours

4

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Course 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 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) 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!

Goals

  • you'll have a good understanding of the most powerful Computer Vision models
  • you'll understand convolutional neural network (CNN) based approaches
  • you'll understand SSD and YOLO state of the art computer vision algorithms
  • you'll understand OpenCV

Prerequisites

  • Basic Python programming skills
Computer Vision Masterclass in Python

Curriculum

Check out the detailed breakdown of what’s inside the course

Environment Setup
3 Lectures
  • play icon Installing Python and PyCharm on Mac
  • play icon Installing OpenCV 02:50 02:50
  • play icon Installing Python and PyCharm on Windows
History of Computer Vision
1 Lectures
Tutorialspoint
Handling Images and Pixels
7 Lectures
Tutorialspoint
Computer Vision Project I - Lane Detection Problem (Self-Driving Cars)
9 Lectures
Tutorialspoint
Viola-Jones Face Detection Algorithm Theory
7 Lectures
Tutorialspoint
Face Detection with Viola-Jones Method Implementation
4 Lectures
Tutorialspoint
Histogram of Oriented Gradients (HOG) Algorithm Theory
6 Lectures
Tutorialspoint
Histogram of Oriented Gradients (HOG) Implementation
5 Lectures
Tutorialspoint
Convolutional Neural Networks (CNNs) Based Approaches
6 Lectures
Tutorialspoint
You Only Look Once (YOLO) Algorithm Theory
8 Lectures
Tutorialspoint
You Only Look Once (YOLO) Algorithm Implementation
7 Lectures
Tutorialspoint
Single-Shot MultiBox Detector (SSD) Theory
7 Lectures
Tutorialspoint
SSD Algorithm Implementation
5 Lectures
Tutorialspoint

Instructor Details

Holczer Balazs

Holczer Balazs

About me


My name is Balazs Holczer. I am from Budapest, Hungary. I am qualified as a physicist. At the moment I am working as a simulation engineer at a multinational company. I have been interested in algorithms and data structures and its implementations especially in Java since university. Later on I got acquainted with machine learning techniques, artificial intelligence, numerical methods and recipes such as solving differential equations, linear algebra, interpolation and extrapolation. These things may prove to be very very important in several fields: software engineering, research and development or investment banking. I have a special addiction to quantitative models such as the Black-Scholes model, or the Merton-model.

Take a look at my website if you are interested in these topics!


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