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Learning OpenCV 3 Computer Vision with Python (Update)

You're reading from   Learning OpenCV 3 Computer Vision with Python (Update) Unleash the power of computer vision with Python using OpenCV

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Product type Paperback
Published in Sep 2015
Publisher
ISBN-13 9781785283840
Length 266 pages
Edition 1st Edition
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Table of Contents (11) Chapters Close

Preface 1. Setting Up OpenCV FREE CHAPTER 2. Handling Files, Cameras, and GUIs 3. Processing Images with OpenCV 3 4. Depth Estimation and Segmentation 5. Detecting and Recognizing Faces 6. Retrieving Images and Searching Using Image Descriptors 7. Detecting and Recognizing Objects 8. Tracking Objects 9. Neural Networks with OpenCV – an Introduction Index

Object detection and recognition techniques


We made a distinction in Chapter 5, Detecting and Recognizing Faces, which we'll reiterate for clarity: detecting an object is the ability of a program to determine if a certain region of an image contains an unidentified object, and recognizing is the ability of a program to identify this object. Recognizing normally only occurs in areas of interest where an object has been detected, for example, we have attempted to recognize faces on the areas of an image that contained a face in the first place.

When it comes to recognizing and detecting objects, there are a number of techniques used in computer vision, which we'll be examining:

  • Histogram of Oriented Gradients

  • Image pyramids

  • Sliding windows

Unlike feature detection algorithms, these are not mutually exclusive techniques, rather, they are complimentary. You can perform a Histogram of Oriented Gradients (HOG) while applying the sliding windows technique.

So, let's take a look at HOG first and understand...

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