Lingua: Inglese
Editore: Springer-verlag Berlin And Heidelberg Gmbh & Co. Kg, Berkley, 2014
ISBN 10: 1430259299 ISBN 13: 9781430259299
Da: Grand Eagle Retail, Bensenville, IL, U.S.A.
Prima edizione
Paperback. Condizione: new. Paperback. Computer Vision Metrics provides an extensive survey and analysis of over 100 current and historical feature description and machine vision methods, with a detailed taxonomy for local, regional and global features. This book provides necessary background to develop intuition about why interest point detectors and feature descriptors actually work, how they are designed, with observations about tuning the methods for achieving robustness and invariance targets for specific applications. The survey is broader than it is deep, with over 540 references provided to dig deeper. The taxonomy includes search methods, spectra components, descriptor representation, shape, distance functions, accuracy, efficiency, robustness and invariance attributes, and more. Rather than providing how-to source code examples and shortcuts, this book provides a counterpoint discussion to the many fine opencv community source code resources available for hands-on practitioners. Computer Vision Metrics provides an extensive survey and analysis of over 100 current and historical feature description and machine vision methods, with a detailed taxonomy for local, regional and global features. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.
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Paperback or Softback. Condizione: New. Computer Vision Metrics: Survey, Taxonomy, and Analysis. Book.
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Lingua: Inglese
Editore: Springer-Verlag Berlin and Heidelberg GmbH & Co. KG, 2014
ISBN 10: 1430259299 ISBN 13: 9781430259299
Da: THE SAINT BOOKSTORE, Southport, Regno Unito
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Aggiungi al carrelloPaperback / softback. Condizione: New. New copy - Usually dispatched within 4 working days.
Condizione: New. pp. 508.
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Aggiungi al carrelloCondizione: New. Num Pages: 539 pages, 216 black & white illustrations, biography. BIC Classification: UYT. Category: (G) General (US: Trade). Dimension: 230 x 154 x 30. Weight in Grams: 726. . 2014. 1st ed. paperback. . . . .
Condizione: New. Num Pages: 539 pages, 216 black & white illustrations, biography. BIC Classification: UYT. Category: (G) General (US: Trade). Dimension: 230 x 154 x 30. Weight in Grams: 726. . 2014. 1st ed. paperback. . . . . Books ship from the US and Ireland.
Lingua: Inglese
Editore: Springer-verlag Berlin And Heidelberg Gmbh & Co. Kg, Berkley, 2014
ISBN 10: 1430259299 ISBN 13: 9781430259299
Da: AussieBookSeller, Truganina, VIC, Australia
Prima edizione
EUR 58,66
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Aggiungi al carrelloPaperback. Condizione: new. Paperback. Computer Vision Metrics provides an extensive survey and analysis of over 100 current and historical feature description and machine vision methods, with a detailed taxonomy for local, regional and global features. This book provides necessary background to develop intuition about why interest point detectors and feature descriptors actually work, how they are designed, with observations about tuning the methods for achieving robustness and invariance targets for specific applications. The survey is broader than it is deep, with over 540 references provided to dig deeper. The taxonomy includes search methods, spectra components, descriptor representation, shape, distance functions, accuracy, efficiency, robustness and invariance attributes, and more. Rather than providing how-to source code examples and shortcuts, this book provides a counterpoint discussion to the many fine opencv community source code resources available for hands-on practitioners. Computer Vision Metrics provides an extensive survey and analysis of over 100 current and historical feature description and machine vision methods, with a detailed taxonomy for local, regional and global features. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.
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Aggiungi al carrelloTaschenbuch. Condizione: Neu. Computer Vision Metrics | Survey, Taxonomy, and Analysis | Scott Krig | Taschenbuch | Einband - flex.(Paperback) | Englisch | 2014 | Apress | EAN 9781430259299 | Verantwortliche Person für die EU: Springer Nature Customer Service Center GmbH, Europaplatz 3, 69115 Heidelberg, productsafety[at]springernature[dot]com | Anbieter: preigu.
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Condizione: New. pp. 655.
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Aggiungi al carrelloPaperback. Condizione: New. In Synthetic Vision: Using Volume Learning and Visual DNA, a holistic model of the human visual system is developed into a working model in C++, informed by the latest neuroscience, DNN, and computer vision research. The author's synthetic visual pathway model includes the eye, LGN, visual cortex, and the high level PFC learning centers. The corresponding visual genome model (VGM), begun in 2014, is introduced herein as the basis for a visual genome project analogous to the Human Genome Project funded by the US government. The VGM introduces volume learning principles and Visual DNA (VDNA) taking a multivariate approach beyond deep neural networks. Volume learning is modeled as programmable learning and reasoning agents, providing rich methods for structured agent classification networks. Volume learning incorporates a massive volume of multivariate features in various data space projections, collected into strands of Visual DNA, analogous to human DNA genes. VGM lays a foundation for a visual genome project to sequence VDNA as visual genomes in a public database, using collaborative research to move synthetic vision science forward and enable new applications. Bibliographical references are provided to key neuroscience, computer vision, and deep learning research, which form the basis for the biologically plausible VGM model and the synthetic visual pathway. The book also includes graphical illustrations and C++ API reference materials to enable VGM application programming. Open source code licenses are available for engineers and scientists. Scott Krig founded Krig Research to provide some of the world's first vision and imaging systems worldwide for military, industry, government, and academic use. Krig has worked for major corporations and startups in the areas of machine learning, computer vision, imaging, graphics, robotics and automation, computer security and cryptography. He has authored international patents in the areas of computer architecture, communications, computer security, digital imaging, and computer vision, and studied at Stanford. Scott Krig is the author of the English/Chinese Springer book Computer Vision Metrics, Survey, Taxonomy and Analysis of Computer Vision, Visual Neuroscience, and Deep Learning, Textbook Edition, as well as other books, articles, and papers.
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Da: California Books, Miami, FL, U.S.A.
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