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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
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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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Summary


In this chapter, we learnt how to convert our image into a feature vector, or a sequence of values. Specifically, we looked at one particular feature descriptor: the local binary pattern (or, LBP) operator. Apart from the traditional LBP formulation, we also saw some common variants that are used. An important takeaway from this chapter is that the LBP captures the "texture" of the input image. We also looked at how such texture information can help us capture the subtle variations present in facial images.

Local binary pattern is just one of the many possible feature descriptors that have been proposed in Computer Vision literature. Some other examples include Histogram of Oriented Gradients (HoG), SIFT and SURF. HoG uses the concept of image derivatives whereas SIFT and SURF are much more sophisticated feature descriptors (they are patented algorithms as well). In fact, similar in spirit to LBP, we have yet another feature descriptor called local ternary patterns (LTP). Instead...

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Learning OpenCV 3 Application Development
Published in: Dec 2016Publisher: PacktISBN-13: 9781784391454

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