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Practical Computer Vision

You're reading from   Practical Computer Vision Extract insightful information from images using TensorFlow, Keras, and OpenCV

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Product type Paperback
Published in Feb 2018
Publisher Packt
ISBN-13 9781788297684
Length 234 pages
Edition 1st Edition
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Author (1):
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Abhinav Dadhich Abhinav Dadhich
Author Profile Icon Abhinav Dadhich
Abhinav Dadhich
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Table of Contents (12) Chapters Close

Preface 1. A Fast Introduction to Computer Vision FREE CHAPTER 2. Libraries, Development Platform, and Datasets 3. Image Filtering and Transformations in OpenCV 4. What is a Feature? 5. Convolutional Neural Networks 6. Feature-Based Object Detection 7. Segmentation and Tracking 8. 3D Computer Vision 9. Mathematics for Computer Vision 10. Machine Learning for Computer Vision 11. Other Books You May Enjoy

What is machine learning?

Let's say we have scanned images of several handwritten digits and want to make a piece of software that would recognize handwritten digits from an image scan. For simplicity, let's assume that we have only one digit. The target software that we develop takes in this image and outputs a number corresponding to that image. We can create an algorithm with several checks, such as: if there is a single vertical line, then output it as 1, or if there is an oval shape, then show it as zero. However, this is very naive and is a bad solution because we can have vertical lines for other digits too: 7, 9, and so on. The following figure explains the overall process, taking in one of the samples from the MNIST handwritten digit dataset:

There are several ways to model such a problem. We know that an image is made up of arrays of pixels and each pixel...

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