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  • Computer Vision Projects with OpenCV and Python 3
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Computer Vision Projects with OpenCV and Python 3

Computer Vision Projects with OpenCV and Python 3

By : Rever
1 (1)
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Computer Vision Projects with OpenCV and Python 3

Computer Vision Projects with OpenCV and Python 3

1 (1)
By: Rever

Overview of this book

Python is the ideal programming language for rapidly prototyping and developing production-grade codes for image processing and Computer Vision with its robust syntax and wealth of powerful libraries. This book will help you design and develop production-grade Computer Vision projects tackling real-world problems. With the help of this book, you will learn how to set up Anaconda and Python for the major OSes with cutting-edge third-party libraries for Computer Vision. You'll learn state-of-the-art techniques for classifying images, finding and identifying human postures, and detecting faces within videos. You will use powerful machine learning tools such as OpenCV, Dlib, and TensorFlow to build exciting projects such as classifying handwritten digits, detecting facial features,and much more. The book also covers some advanced projects, such as reading text from license plates from real-world images using Google’s Tesseract software, and tracking human body poses using DeeperCut within TensorFlow. By the end of this book, you will have the expertise required to build your own Computer Vision projects using Python and its associated libraries.
Table of Contents (9 chapters)
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Handwritten Digit Recognition with scikit-learn and TensorFlow

In this chapter, we are going to learn how machine learning can be applied to computer vision projects, using a couple of different Python modules. We will also create and train a support vector machine that will actually perform our digit classification.

In this chapter, we will be covering the following topics:

  • Acquiring and processing MNIST digit data
  • Creating and training a support vector machine
  • Applying the support vector machine to new data
  • Introducing TensorFlow with digit classification
  • Evaluating the results
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