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You're reading from  Learning OpenCV 3 Application Development

Product typeBook
Published inDec 2016
Reading LevelIntermediate
PublisherPackt
ISBN-139781784391454
Edition1st Edition
Languages
C++
Tools
OpenCV
Concepts
Computer Vision
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Author (1)
Samyak Datta
Samyak Datta
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Samyak Datta

Samyak Datta has a bachelor's and a master's degree in Computer Science from the Indian Institute of Technology, Roorkee. He is a computer vision and machine learning enthusiast. His first contact with OpenCV was in 2013 when he was working on his master's thesis, and since then, there has been no looking back. He has contributed to OpenCV's GitHub repository. Over the course of his undergraduate and master's degrees, Samyak has had the opportunity to engage with both the industry and research. He worked with Google India and Media.net (Directi) as a software engineering intern, where he was involved with projects ranging from machine learning and natural language processing to computer vision. As of 2016, he is working at the Center for Visual Information Technology (CVIT) at the Indian Institute of Information Technology, Hyderabad.
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What does LBP capture?

We have described how the LBP operator is applied to images, and we have also discussed some variants of it. Now obviously, if we are studying LBP in such great detail, we would definitely be applying it somewhere! We are going to use the LBP operator on the (cropped and aligned) facial images that we obtained in the last chapter. But before we jump in and start running the LBP code on our face images, let's take a step back and ponder upon a very important question, "What does the LBP capture?"

We have already gone through the mechanics of calculating the LBP code for a pixel, and we have seen that the end result is a 256 (or a 58) dimensional histogram. But, what does that histogram tell us about the image? Well, you can say that histograms give us frequency counts and you would be right. It was easy to visualize this in the case of the image histograms from Chapter 4, Image Histograms where the histogram bins were the grayscale values. Hence, it was easy to intuitively...

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Learning OpenCV 3 Application Development
Published in: Dec 2016Publisher: PacktISBN-13: 9781784391454
© 2016 Packt Publishing Limited All Rights Reserved

Author (1)

author image
Samyak Datta

Samyak Datta has a bachelor's and a master's degree in Computer Science from the Indian Institute of Technology, Roorkee. He is a computer vision and machine learning enthusiast. His first contact with OpenCV was in 2013 when he was working on his master's thesis, and since then, there has been no looking back. He has contributed to OpenCV's GitHub repository. Over the course of his undergraduate and master's degrees, Samyak has had the opportunity to engage with both the industry and research. He worked with Google India and Media.net (Directi) as a software engineering intern, where he was involved with projects ranging from machine learning and natural language processing to computer vision. As of 2016, he is working at the Center for Visual Information Technology (CVIT) at the Indian Institute of Information Technology, Hyderabad.
Read more about Samyak Datta

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