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Learning OpenCV 4 Computer Vision with Python 3

Learning OpenCV 4 Computer Vision with Python 3

By : Joseph Howse, Joe Minichino
4.1 (14)
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Learning OpenCV 4 Computer Vision with Python 3

Learning OpenCV 4 Computer Vision with Python 3

4.1 (14)
By: Joseph Howse, Joe Minichino

Overview of this book

Computer vision is a rapidly evolving science, encompassing diverse applications and techniques. This book will not only help those who are getting started with computer vision but also experts in the domain. You’ll be able to put theory into practice by building apps with OpenCV 4 and Python 3. You’ll start by understanding OpenCV 4 and how to set it up with Python 3 on various platforms. Next, you’ll learn how to perform basic operations such as reading, writing, manipulating, and displaying still images, videos, and camera feeds. From taking you through image processing, video analysis, and depth estimation and segmentation, to helping you gain practice by building a GUI app, this book ensures you’ll have opportunities for hands-on activities. Next, you’ll tackle two popular challenges: face detection and face recognition. You’ll also learn about object classification and machine learning concepts, which will enable you to create and use object detectors and classifiers, and even track objects in movies or video camera feed. Later, you’ll develop your skills in 3D tracking and augmented reality. Finally, you’ll cover ANNs and DNNs, learning how to develop apps for recognizing handwritten digits and classifying a person's gender and age. By the end of this book, you’ll have the skills you need to execute real-world computer vision projects.
Table of Contents (13 chapters)
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Recognizing handwritten digits with an ANN

A handwritten digit is any of the 10 Arabic numerals (0 to 9), written manually with a pen or pencil, as opposed to being printed by a machine. The appearance of handwritten digits can vary significantly. Different people have different handwriting, and with the possible exception of a skilled calligrapher a person does not produce identical digits every time he or she writes. This variability means that the visual recognition of handwritten digits is a non-trivial problem for machine learning. Indeed, students and researchers in machine learning often test their skills and new algorithms by attempting to train an accurate recognizer for handwritten digits. We will approach this challenge in the following manner:

  1. Load data from a Python-friendly version of the MNIST database. This is a widely used database containing...
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