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OpenCV 4 with Python Blueprints

You're reading from   OpenCV 4 with Python Blueprints Build creative computer vision projects with the latest version of OpenCV 4 and Python 3

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Product type Paperback
Published in Mar 2020
Publisher Packt
ISBN-13 9781789801811
Length 366 pages
Edition 2nd Edition
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Authors (4):
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Michael Beyeler (USD) Michael Beyeler (USD)
Author Profile Icon Michael Beyeler (USD)
Michael Beyeler (USD)
Dr. Menua Gevorgyan Dr. Menua Gevorgyan
Author Profile Icon Dr. Menua Gevorgyan
Dr. Menua Gevorgyan
Michael Beyeler Michael Beyeler
Author Profile Icon Michael Beyeler
Michael Beyeler
Arsen Mamikonyan Arsen Mamikonyan
Author Profile Icon Arsen Mamikonyan
Arsen Mamikonyan
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Toc

Table of Contents (14) Chapters Close

Preface 1. Fun with Filters 2. Hand Gesture Recognition Using a Kinect Depth Sensor FREE CHAPTER 3. Finding Objects via Feature Matching and Perspective Transforms 4. 3D Scene Reconstruction Using Structure from Motion 5. Using Computational Photography with OpenCV 6. Tracking Visually Salient Objects 7. Learning to Recognize Traffic Signs 8. Learning to Recognize Facial Emotions 9. Learning to Classify and Localize Objects 10. Learning to Detect and Track Objects 11. Profiling and Accelerating Your Apps 12. Setting Up a Docker Container 13. Other Books You May Enjoy

Understanding visual saliency

Visual saliency is a technical term from cognitive psychology that tries to describe the visual quality of certain objects or items that allows them to grab our immediate attention. Our brains constantly drive our gaze toward the important regions of the visual scene and keep track of them over time, allowing us to quickly scan our surroundings for interesting objects and events while neglecting the less important parts.

An example of a regular RGB image and its conversion to a saliency map, where the statistically interesting pop-out regions appear bright and the others dark, is shown in the following screenshot:

Fourier analysis will enable us to get a general understanding of natural image statistics, which will help us build a model of what general image backgrounds look like. By comparing and contrasting the background model to a specific image...

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