46,95 €*
Versandkostenfrei per Post / DHL
Lieferzeit 2-4 Werktage
Topics and features:
Describes essential tools for image mining, covering Fourier transforms, Gabor filters, and contemporary wavelet transforms
Develops many new exercises (most with MATLAB code and instructions)
Includes review summaries at the end of each chapter
Analyses state-of-the-art models, algorithms, and procedures for image mining
Integrates new sections on pre-processing, discrete cosine transform, and statistical inference and testing
Demonstrates how features like color, texture, and shape can be mined or extracted for image representation
Applies powerful classification approaches: Bayesian classification, support vector machines, neural networks, and decision trees
Implements imaging techniques for indexing, ranking, and presentation, as well as database visualization
This easy-to-follow, award-winning book illuminates how concepts from fundamental and advanced mathematics can be applied to solve a broad range of image data mining problems encountered by students and researchers of computer science. Students of mathematics and other scientific disciplines will also benefit from the applications and solutions described in the text, together with the hands-on exercises that enable the reader to gain first-hand experience of computing.
Topics and features:
Describes essential tools for image mining, covering Fourier transforms, Gabor filters, and contemporary wavelet transforms
Develops many new exercises (most with MATLAB code and instructions)
Includes review summaries at the end of each chapter
Analyses state-of-the-art models, algorithms, and procedures for image mining
Integrates new sections on pre-processing, discrete cosine transform, and statistical inference and testing
Demonstrates how features like color, texture, and shape can be mined or extracted for image representation
Applies powerful classification approaches: Bayesian classification, support vector machines, neural networks, and decision trees
Implements imaging techniques for indexing, ranking, and presentation, as well as database visualization
This easy-to-follow, award-winning book illuminates how concepts from fundamental and advanced mathematics can be applied to solve a broad range of image data mining problems encountered by students and researchers of computer science. Students of mathematics and other scientific disciplines will also benefit from the applications and solutions described in the text, together with the hands-on exercises that enable the reader to gain first-hand experience of computing.
Dr. Dengsheng Zhang is Senior Lecturer in the School of Engineering, Information Technology and Physical Sciences at Federation University Australia and a Guest Professor of Xi'an University of Posts & Telecommunications, China. He is on the list of Top 2% Scientists in the World ranked by Stanford University. Dr Zhang was the Textbook & Academic Authors Association's winner of their 2020 Most Promising New Textbook Award, with the judges noting:
"Fundamentals of Image Data Mining provides excellent coverage of current algorithms and techniques in image analysis. It does this using a progression of essential and novel image processing tools that give students an in-depth understanding of how the tools fit together and how to apply them to problems."
Presents a complete introduction to image data mining, and a treasure trove of cutting-edge techniques in image data mining
Describes the applied mathematics and mathematical modeling in an engaging style, complete with an accessible introduction to the foundational and engineering mathematics
Offers a shortcut entry into AI and machine learning, introducing four major machine learning tools with gentle mathematics
1.Fourier Transform.- 2. Windowed Fourier Transform.- 3. Wavelet Transform.- 4. Color Feature Extraction.- 5. Texture Feature Extraction.- 6. Shape Representation.- 7. Bayesian Classification.- Support Vector Machines.- 8. Artificial Neural Networks.- 9. Image Annotation with Decision Trees.-10. Image Indexing.- 11. Image Ranking.- 12. Image Presentation.- 13. Appendix.
| Erscheinungsjahr: | 2022 |
|---|---|
| Fachbereich: | EDV |
| Genre: | Informatik, Mathematik, Medizin, Naturwissenschaften, Technik |
| Rubrik: | Naturwissenschaften & Technik |
| Thema: | Lexika |
| Medium: | Taschenbuch |
| Inhalt: |
xxxiii
363 S. 112 s/w Illustr. 131 farbige Illustr. 363 p. 243 illus. 131 illus. in color. |
| ISBN-13: | 9783030692537 |
| ISBN-10: | 3030692531 |
| Sprache: | Englisch |
| Einband: | Kartoniert / Broschiert |
| Autor: | Zhang, Dengsheng |
| Auflage: | Second Edition 2021 |
| Hersteller: | Springer International Publishing |
| Verantwortliche Person für die EU: | Springer Verlag GmbH, Tiergartenstr. 17, D-69121 Heidelberg, juergen.hartmann@springer.com |
| Maße: | 235 x 155 x 22 mm |
| Von/Mit: | Dengsheng Zhang |
| Erscheinungsdatum: | 27.06.2022 |
| Gewicht: | 0,604 kg |
Dr. Dengsheng Zhang is Senior Lecturer in the School of Engineering, Information Technology and Physical Sciences at Federation University Australia and a Guest Professor of Xi'an University of Posts & Telecommunications, China. He is on the list of Top 2% Scientists in the World ranked by Stanford University. Dr Zhang was the Textbook & Academic Authors Association's winner of their 2020 Most Promising New Textbook Award, with the judges noting:
"Fundamentals of Image Data Mining provides excellent coverage of current algorithms and techniques in image analysis. It does this using a progression of essential and novel image processing tools that give students an in-depth understanding of how the tools fit together and how to apply them to problems."
Presents a complete introduction to image data mining, and a treasure trove of cutting-edge techniques in image data mining
Describes the applied mathematics and mathematical modeling in an engaging style, complete with an accessible introduction to the foundational and engineering mathematics
Offers a shortcut entry into AI and machine learning, introducing four major machine learning tools with gentle mathematics
1.Fourier Transform.- 2. Windowed Fourier Transform.- 3. Wavelet Transform.- 4. Color Feature Extraction.- 5. Texture Feature Extraction.- 6. Shape Representation.- 7. Bayesian Classification.- Support Vector Machines.- 8. Artificial Neural Networks.- 9. Image Annotation with Decision Trees.-10. Image Indexing.- 11. Image Ranking.- 12. Image Presentation.- 13. Appendix.
| Erscheinungsjahr: | 2022 |
|---|---|
| Fachbereich: | EDV |
| Genre: | Informatik, Mathematik, Medizin, Naturwissenschaften, Technik |
| Rubrik: | Naturwissenschaften & Technik |
| Thema: | Lexika |
| Medium: | Taschenbuch |
| Inhalt: |
xxxiii
363 S. 112 s/w Illustr. 131 farbige Illustr. 363 p. 243 illus. 131 illus. in color. |
| ISBN-13: | 9783030692537 |
| ISBN-10: | 3030692531 |
| Sprache: | Englisch |
| Einband: | Kartoniert / Broschiert |
| Autor: | Zhang, Dengsheng |
| Auflage: | Second Edition 2021 |
| Hersteller: | Springer International Publishing |
| Verantwortliche Person für die EU: | Springer Verlag GmbH, Tiergartenstr. 17, D-69121 Heidelberg, juergen.hartmann@springer.com |
| Maße: | 235 x 155 x 22 mm |
| Von/Mit: | Dengsheng Zhang |
| Erscheinungsdatum: | 27.06.2022 |
| Gewicht: | 0,604 kg |
Ähnliche Produkte
Lieferzeit 1-2 Wochen
Lieferzeit 2-4 Werktage
Lieferzeit 1-2 Wochen
Lieferzeit 1-2 Wochen
Lieferzeit 1-2 Wochen
Lieferzeit 2-4 Werktage
Lieferzeit 4-7 Werktage