Computer vision : principles, algorithms, applications, learning /

""Computer vision ..." (previously entitled Computer and machine vision) clearly and systematically presents the basic methodology of computer vision, covering the essential elements of the theory while emphasizing algorithmic and practical design constraints. This fully revised fifth...

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Detalles Bibliográficos
Autor principal: Davies, E. R. (E. Roy)
Formato: Libro
Lenguaje:Inglés
Publicado: London ; Cambridge, MA : Elsevier : Academic Press, c2018.
Edición:5th ed.
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Aporte de:Registro referencial: Solicitar el recurso aquí
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020 |a 9780128092842 
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035 |a (OCoLC)1049576999 
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050 4 |a TA1634  |b .D38 2018 
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100 1 |a Davies, E. R.  |q (E. Roy) 
240 1 0 |a Computer and machine vision 
245 1 0 |a Computer vision :  |b principles, algorithms, applications, learning /  |c E.R. Davies. 
250 |a 5th ed. 
260 |a London ;  |a Cambridge, MA :  |b Elsevier :  |b Academic Press,  |c c2018. 
300 |a xlii, 858 p. :  |b il. ;  |c 24 cm. 
500 |a Edición previa: Computer and machine vision / 4th ed. 2012. 
504 |a Incluye referencias bibliográficas (p. 801-845) e índice. 
505 0 |a Foreword -- Preface to the fifth edition -- Preface to the first edition -- Topics covered in application case studies -- Glossary of acronyms and abbreviations -- 1. Vision, the challenge -- Part I. Low-level vision: 2 Images and imaging operations -- 3. Image filtering and morphology -- 4. The role of thresholding -- 5. Edge detection -- 6. Corner, interest point and invariant feature detection -- 7. Texture analysis -- Part II. Intermediate-level vision: 8. Binary shape analysis -- 9. Boundary pattern analysis -- 10. Line, circle and ellipse detection -- 11. The generalised Hough transform -- 12. Object segmentation and shape models -- Part III. Machine learning and deep learning networks: 13. Basic classification concepts -- 14. Machine learning: probabilistic methods -- 15. Deep learning networks -- Part IV. 3D vision and motion: 16. The three-dimensional world -- 17. Tackling the perspective n-point problem -- 18. Invariants and perspective -- 19. Image transformations and camera calibration -- 20. Motion -- Part V. Putting computer vision to work: 21. Face detection and recognition: the impact of deep learning -- 22. Surveillance -- 23. In-vehicle vision systems -- 24. Epilogue: Perspectives in vision -- Appendix A: Robust statistics -- Appendix B: The Sampling Theorem -- Appendix C: The representation of colour -- Appendix D: Sampling from distributions. 
520 |a ""Computer vision ..." (previously entitled Computer and machine vision) clearly and systematically presents the basic methodology of computer vision, covering the essential elements of the theory while emphasizing algorithmic and practical design constraints. This fully revised fifth edition has brought in more of the concepts and applications of computer vision, making it a very comprehensive and up-to-date text suitable for undergraduate and graduate students, researchers and R&D engineers working in this vibrant subject." --Descripción del editor. 
650 0 |a Computer vision. 
650 0 |a Information visualization. 
650 7 |a Visión por computadora.  |2 UDESA 
650 7 |a Visualización de la información.  |2 UDESA