Computer Vision with Python 3

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

Computer Vision with Python 3

Saurabh Kapur

3 customer reviews
Unleash the power of computer vision with Python to carry out image processing and computer vision techniques
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Book Details

ISBN 139781788299763
Paperback206 pages

Book Description

This book is a thorough guide for developers who want to get started with building computer vision applications using Python 3. The book is divided into five sections: The Fundamentals of Image Processing, Applied Computer Vision, Making Applications Smarter,Extending your Capabilities using OpenCV, and Getting Hands on. Throughout this book, three image processing libraries Pillow, Scikit-Image, and OpenCV will be used to implement different computer vision algorithms.

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

Table of Contents

Chapter 1: Introduction to Image Processing
Image processing - its applications
Image processing libraries
Summary
Chapter 2: Filters and Features
Image derivatives
Convolution
Understanding image filters
Edge detection
Summary
Chapter 3: Drilling Deeper into Features - Object Detection
Revisiting image features
Harris corner detection
Local Binary Patterns
Oriented FAST and Rotated BRIEF (ORB)
Image stitching
Summary
Chapter 4: Segmentation - Understanding Images Better
Introduction to segmentation
Contour detection
The Watershed algorithm
Superpixels
Normalized graph cut
Summary
Chapter 5: Integrating Machine Learning with Computer Vision
Introduction to machine learning
Applications of machine learning for computer vision
Logistic regression
Support vector machines
K-means clustering
Summary
Chapter 6: Image Classification Using Neural Networks
Introduction to neural networks
Convolutional neural networks
Challenges in machine learning
Summary
Chapter 7: Introduction to Computer Vision using OpenCV
Installation
OpenCV APIs
Summary
Chapter 8: Object Detection Using OpenCV
Haar Cascades
Scale Invariant Feature Transformation (SIFT)
Speeded up robust features
Summary
Chapter 9: Video Processing Using OpenCV
Reading/writing videos
Basic operations on videos
Color tracking
Object tracking
Summary
Chapter 10: Computer Vision as a Service
Computer vision as a service – architecture overview
Environment setup
Developing a server-client model
Computer vision engine
Putting it all together
Summary

What You Will Learn

  • Working with open source libraries such Pillow, Scikit-image, and OpenCV
  • Writing programs such as edge detection, color processing, image feature extraction, and more
  • Implementing feature detection algorithms like LBP and ORB
  • Tracking objects using an external camera or a video file
  • Optical Character Recognition using Machine Learning.
  • Understanding Convolutional Neural Networks to learn patterns in images
  • Leveraging Cloud Infrastructure to provide Computer Vision as a Service

Authors

Table of Contents

Chapter 1: Introduction to Image Processing
Image processing - its applications
Image processing libraries
Summary
Chapter 2: Filters and Features
Image derivatives
Convolution
Understanding image filters
Edge detection
Summary
Chapter 3: Drilling Deeper into Features - Object Detection
Revisiting image features
Harris corner detection
Local Binary Patterns
Oriented FAST and Rotated BRIEF (ORB)
Image stitching
Summary
Chapter 4: Segmentation - Understanding Images Better
Introduction to segmentation
Contour detection
The Watershed algorithm
Superpixels
Normalized graph cut
Summary
Chapter 5: Integrating Machine Learning with Computer Vision
Introduction to machine learning
Applications of machine learning for computer vision
Logistic regression
Support vector machines
K-means clustering
Summary
Chapter 6: Image Classification Using Neural Networks
Introduction to neural networks
Convolutional neural networks
Challenges in machine learning
Summary
Chapter 7: Introduction to Computer Vision using OpenCV
Installation
OpenCV APIs
Summary
Chapter 8: Object Detection Using OpenCV
Haar Cascades
Scale Invariant Feature Transformation (SIFT)
Speeded up robust features
Summary
Chapter 9: Video Processing Using OpenCV
Reading/writing videos
Basic operations on videos
Color tracking
Object tracking
Summary
Chapter 10: Computer Vision as a Service
Computer vision as a service – architecture overview
Environment setup
Developing a server-client model
Computer vision engine
Putting it all together
Summary

Book Details

ISBN 139781788299763
Paperback206 pages
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