Python 3.x for Computer Vision [Video]

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Python 3.x for Computer Vision [Video]

Saurabh Kapur

Unleash the power of computer vision with Python to carry out image processing and computer vision techniques
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Video Details

ISBN 139781788838207
Course Length1 hour and 29 minutes

Video Description

This video course is a practical guide for developers who want to get started with building computer vision applications using Python 3. The video is divided into six sections:

  • The Fundamentals of Image Processing 
  • Applied Computer Vision
  • Object detection
  • Making Applications Smarter
  • Extending your Capabilities using OpenCV
  • Getting Hands on

Throughout this video course, three image processing libraries: Pillow, Scikit-Image, and OpenCV are used to implement different computer vision algorithms.

The course will help you build Computer Vision applications that are capable of working in real-world scenarios effectively. Some of the applications that we look at in the course are Optical Character Recognition, Object Tracking and building a Computer Vision as a Service platform that works over the internet.

Style and Approach

Each stage of the course elaborates on various concepts and algorithms in image processing/computer vision using Python. This step-by-step practical guide can be used both as a tutorial and as a reference.

Table of Contents

Introduction to Image Processing
The Course Overview
Image Processing and Its Applications
Image Processing Libraries – Pillow
Geometrical Transformation – Pillow
Introduction to scikit-image
Filters and Features
Image Derivatives
Understanding Image Filters
Custom Filters and Image Thresholding
Edge Detection
Object Detection
Harris Corner Detection
Local Binary Patterns
Oriented FAST and Rotated BRIEF (ORB)
Image Stitching
Segmentation – Understanding Images Better
Contour Detection and the Watershed Algorithm
Superpixels and Normalized Graph Cut
Integrating Machine Learning with Computer Vision
Introduction to Machine Learning
Logistic Regression
Support Vector Machines
K-means Clustering
Image Classification Using Neural Networks
Introduction to Neural Network
MNIST Digit Classification Using Neural Networks
Convolutional Neural Networks

What You Will Learn

  • Work with open source libraries such Pillow, Scikit-image, and OpenCV
  • Write programs such as edge detection, color processing, image feature extraction, and more
  • Implement feature detection algorithms such as LBP and ORB
  • Understand Convolutional Neural Networks to learn patterns in images

Authors

Table of Contents

Introduction to Image Processing
The Course Overview
Image Processing and Its Applications
Image Processing Libraries – Pillow
Geometrical Transformation – Pillow
Introduction to scikit-image
Filters and Features
Image Derivatives
Understanding Image Filters
Custom Filters and Image Thresholding
Edge Detection
Object Detection
Harris Corner Detection
Local Binary Patterns
Oriented FAST and Rotated BRIEF (ORB)
Image Stitching
Segmentation – Understanding Images Better
Contour Detection and the Watershed Algorithm
Superpixels and Normalized Graph Cut
Integrating Machine Learning with Computer Vision
Introduction to Machine Learning
Logistic Regression
Support Vector Machines
K-means Clustering
Image Classification Using Neural Networks
Introduction to Neural Network
MNIST Digit Classification Using Neural Networks
Convolutional Neural Networks

Video Details

ISBN 139781788838207
Course Length1 hour and 29 minutes
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