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Book details
File Size: 9.92 MB
Format: pdf
Print Length: 208 pages
Page Numbers Source ISBN: 1788299760
Publisher: Packt Publishing; 1 edition (August 24, 2017)
Publication Date: August 24, 2017
Sold by: Amazon Digital Services LLC
Language: English
ASIN: B072LN59CJ
Key Features
Learn how to build a full-fledged image processing application using free tools and libraries
Perform basic to advanced image and video stream processing with OpenCV’s Python APIs
Understand and optimize various features of OpenCV with the help of easy-to-grasp examples
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,ExtendingyourCapabilitiesusingOpenCV,andGettingHandson. 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.
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
Table of Contents
Introduction to Image
Processing Filters and Features
Drilling Deeper into features- detecting objects
Segmentation – Understanding Images Better Integrating Machine Learning with Computer Vision
Image Classification using Neural Networks
Introduction to Computer Vision using OpenCV Object Detection using OpenCV
Video Processing using open CV Computer Vision as a Service
Book details
File Size: 9.92 MB
Format: pdf
Print Length: 208 pages
Page Numbers Source ISBN: 1788299760
Publisher: Packt Publishing; 1 edition (August 24, 2017)
Publication Date: August 24, 2017
Sold by: Amazon Digital Services LLC
Language: English
ASIN: B072LN59CJ
Key Features
Learn how to build a full-fledged image processing application using free tools and libraries
Perform basic to advanced image and video stream processing with OpenCV’s Python APIs
Understand and optimize various features of OpenCV with the help of easy-to-grasp examples
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,ExtendingyourCapabilitiesusingOpenCV,andGettingHandson. 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.
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
Table of Contents
Introduction to Image
Processing Filters and Features
Drilling Deeper into features- detecting objects
Segmentation – Understanding Images Better Integrating Machine Learning with Computer Vision
Image Classification using Neural Networks
Introduction to Computer Vision using OpenCV Object Detection using OpenCV
Video Processing using open CV Computer Vision as a Service