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Books > Computing & IT > Applications of computing > Image processing

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Computer Vision Methods for Fast Image Classification and Retrieval (Paperback, 1st ed. 2020) Loot Price: R2,293
Discovery Miles 22 930
You Save: R173 (7%)
Computer Vision Methods for Fast Image Classification and Retrieval (Paperback, 1st ed. 2020): Rafal Scherer

Computer Vision Methods for Fast Image Classification and Retrieval (Paperback, 1st ed. 2020)

Rafal Scherer

Series: Studies in Computational Intelligence, 821

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List price R2,466 Loot Price R2,293 Discovery Miles 22 930 | Repayment Terms: R215 pm x 12* You Save R173 (7%)

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The book presents selected methods for accelerating image retrieval and classification in large collections of images using what are referred to as 'hand-crafted features.' It introduces readers to novel rapid image description methods based on local and global features, as well as several techniques for comparing images. Developing content-based image comparison, retrieval and classification methods that simulate human visual perception is an arduous and complex process. The book's main focus is on the application of these methods in a relational database context. The methods presented are suitable for both general-type and medical images. Offering a valuable textbook for upper-level undergraduate or graduate-level courses on computer science or engineering, as well as a guide for computer vision researchers, the book focuses on techniques that work under real-world large-dataset conditions.

General

Imprint: Springer Nature Switzerland AG
Country of origin: Switzerland
Series: Studies in Computational Intelligence, 821
Release date: October 2020
First published: 2020
Authors: Rafal Scherer
Dimensions: 235 x 155mm (L x W)
Format: Paperback
Pages: 137
Edition: 1st ed. 2020
ISBN-13: 978-3-03-012197-6
Categories: Books > Computing & IT > Applications of computing > Artificial intelligence > Computer vision
Books > Computing & IT > Applications of computing > Image processing > General
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LSN: 3-03-012197-6
Barcode: 9783030121976

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