Optics and Artificial Vision. (Record no. 36774)
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| fixed length control field | 06754nam a22004933i 4500 |
| 001 - CONTROL NUMBER | |
| control field | EBC31252986 |
| 003 - CONTROL NUMBER IDENTIFIER | |
| control field | MiAaPQ |
| 005 - DATE AND TIME OF LATEST TRANSACTION | |
| control field | 20240724115946.0 |
| 006 - FIXED-LENGTH DATA ELEMENTS--ADDITIONAL MATERIAL CHARACTERISTICS | |
| fixed length control field | m o d | |
| 007 - PHYSICAL DESCRIPTION FIXED FIELD--GENERAL INFORMATION | |
| fixed length control field | cr cnu|||||||| |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION | |
| fixed length control field | 240724s2021 xx o ||||0 eng d |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER | |
| International Standard Book Number | 9780750346344 |
| Qualifying information | (electronic bk.) |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER | |
| Canceled/invalid ISBN | 9780750337083 |
| 035 ## - SYSTEM CONTROL NUMBER | |
| System control number | (MiAaPQ)EBC31252986 |
| 035 ## - SYSTEM CONTROL NUMBER | |
| System control number | (Au-PeEL)EBL31252986 |
| 035 ## - SYSTEM CONTROL NUMBER | |
| System control number | (OCoLC)1429723452 |
| 040 ## - CATALOGING SOURCE | |
| Original cataloging agency | MiAaPQ |
| Language of cataloging | eng |
| Description conventions | rda |
| -- | pn |
| Transcribing agency | MiAaPQ |
| Modifying agency | MiAaPQ |
| 050 #4 - LIBRARY OF CONGRESS CALL NUMBER | |
| Classification number | TA1634 .G669 2021 |
| 082 0# - DEWEY DECIMAL CLASSIFICATION NUMBER | |
| Classification number | 006.37 |
| 100 1# - MAIN ENTRY--PERSONAL NAME | |
| Personal name | González-Acuña, Rafael G. |
| 245 10 - TITLE STATEMENT | |
| Title | Optics and Artificial Vision. |
| 250 ## - EDITION STATEMENT | |
| Edition statement | 1st ed. |
| 264 #1 - PRODUCTION, PUBLICATION, DISTRIBUTION, MANUFACTURE, AND COPYRIGHT NOTICE | |
| Place of production, publication, distribution, manufacture | Bristol : |
| Name of producer, publisher, distributor, manufacturer | Institute of Physics Publishing, |
| Date of production, publication, distribution, manufacture, or copyright notice | 2021. |
| 264 #4 - PRODUCTION, PUBLICATION, DISTRIBUTION, MANUFACTURE, AND COPYRIGHT NOTICE | |
| Date of production, publication, distribution, manufacture, or copyright notice | ©2021. |
| 300 ## - PHYSICAL DESCRIPTION | |
| Extent | 1 online resource (231 pages) |
| 336 ## - CONTENT TYPE | |
| Content type term | text |
| Content type code | txt |
| Source | rdacontent |
| 337 ## - MEDIA TYPE | |
| Media type term | computer |
| Media type code | c |
| Source | rdamedia |
| 338 ## - CARRIER TYPE | |
| Carrier type term | online resource |
| Carrier type code | cr |
| Source | rdacarrier |
| 490 1# - SERIES STATEMENT | |
| Series statement | IOP Series in Emerging Technologies in Optics and Photonics Series |
| 505 0# - FORMATTED CONTENTS NOTE | |
| Formatted contents note | Intro -- Preface -- Acknowledgements -- Acknowledgements of Rafael G González-Acuña -- Acknowledgements of Héctor A Chaparro-Romo -- Acknowledgements of Israel Melendez-Montoya -- Author biographies -- Rafael G González-Acuña -- Héctor A Chaparro-Romo -- Israel Melendez-Montoya -- Chapter 1 Optics, sensors and images -- 1.1 Introduction -- 1.2 Optics and images -- 1.3 Vision -- 1.4 Optical instruments and optical design -- 1.5 Cameras -- 1.6 CCD sensor -- 1.7 CMOS sensor -- 1.8 Python as a program language for this book -- 1.9 Artificial vision and computer vision -- 1.10 End notes -- References -- Chapter 2 Introduction to computer vision -- 2.1 Loading and saving images -- 2.2 Image basics -- 2.3 Colour spaces -- 2.4 Basic image processing -- 2.4.1 Translation -- 2.4.2 Rotation -- 2.5 Resizing images -- 2.5.1 Flipping -- 2.5.2 Cropping -- 2.5.3 Image arithmetic -- 2.5.4 Masking -- 2.6 Kernels and morphological operations -- 2.6.1 Erosion and dilatation -- 2.7 Blurring -- 2.8 Thresholding -- 2.9 Gradients and edge detection -- 2.9.1 Gradients -- 2.9.2 Edges -- 2.10 Histograms -- 2.11 End notes -- References -- Chapter 3 Optical flow -- 3.1 Introduction -- 3.2 The Lucas-Kanade algorithm -- 3.2.1 Assumptions -- 3.2.2 The theory behind the Lucas-Kanade algorithm -- 3.2.3 The Lucas-Kanade algorithm step by step -- 3.2.4 Failures of the Lucas-Kanade algorithm -- 3.3 Application of the Lucas-Kanade algorithm and its Python code -- 3.4 The optical flow model -- 3.5 The Horn-Schunck algorithm -- 3.5.1 The smoothness principle -- 3.5.2 The mathematical model -- 3.6 End notes -- References -- Chapter 4 Object detection algorithms -- 4.1 Object detection -- 4.1.1 Statistical interpretation of correlation -- 4.1.2 Fourier interpretation of correlation -- 4.2 Sliding windows and image pyramids -- 4.3 The histogram of oriented gradients descriptor. |
| 505 8# - FORMATTED CONTENTS NOTE | |
| Formatted contents note | 4.4 Support vector machine -- 4.4.1 The concepts behind the SVM -- 4.5 End notes -- References -- Chapter 5 Image descriptors -- 5.1 Introduction to image descriptors -- 5.2 Basic statistics -- 5.3 Hu moments -- 5.4 Zernike moments -- 5.5 Haralick features -- 5.6 Local binary patterns -- 5.7 Keypoint detectors -- 5.7.1 FAST -- 5.7.2 The Harris method -- 5.7.3 GFTT -- 5.7.4 DoG -- 5.7.5 Fast Hessian -- 5.7.6 STAR -- 5.7.7 MSER -- 5.7.8 BRISK -- 5.7.9 ORB -- 5.8 Local invariant descriptors -- 5.8.1 SIFT -- 5.8.2 SURF -- 5.9 Binary descriptors -- 5.9.1 BRIEF -- 5.9.2 ORB binary descriptor -- 5.9.3 The BRISK binary descriptor -- 5.9.4 FREAK -- 5.10 End notes -- References -- Chapter 6 Neural networks -- 6.1 Introduction -- 6.2 Neural networks in a nutshell -- 6.3 Single perceptron learning -- 6.3.1 Continuous activation function perceptron -- 6.3.2 Single perceptron implementation -- 6.4 Multilayer perceptrons -- 6.4.1 Backpropagation -- 6.4.2 Maximum likelihood-binary cross-entropy -- 6.4.3 Maximum likelihood-multiple category cross-entropy -- 6.5 Convolutional neural networks -- 6.5.1 Introduction -- 6.5.2 Convolution and cross-correlation -- 6.5.3 Why CNNs instead of MLPs? -- 6.6 Metrics -- 6.7 CNN architectures -- 6.8 Transfer learning -- 6.9 End notes -- References -- Chapter 7 Optical character recognition -- 7.1 Introduction -- 7.2 Problems in classical OCR -- 7.3 The basic scheme of a classical OCR algorithm -- 7.3.1 Binarization -- 7.3.2 Fragmentation or segmentation of the image -- 7.3.3 Component thinning -- 7.3.4 Comparison with patterns -- 7.4 Classical OCR using machine learning -- 7.5 Modern OCR with deep learning -- 7.5.1 Handwritten text recognition -- 7.5.2 Indexing with databases -- 7.6 OCR with Tesseract -- 7.7 End notes -- References -- Chapter 8 Facial recognition -- 8.1 Introduction to facial recognition. |
| 505 8# - FORMATTED CONTENTS NOTE | |
| Formatted contents note | 8.2 Local binary patterns for facial recognition -- 8.3 The eigenfaces algorithm -- 8.4 Example using the CALTECH faces dataset -- 8.4.1 Create a personal dataset -- 8.5 A LBP face recognizer for your own face -- 8.6 Deep learning facial recognition -- 8.6.1 Face extraction -- 8.7 End notes -- References -- Chapter 9 Artificial vision case studies -- 9.1 Measuring the camera-object distance -- 9.1.1 Camera distortion calibration -- 9.1.2 Using camera sensor size or a previous distance -- 9.2 Single image depth estimation -- 9.2.1 Consistent video depth estimation -- 9.2.2 Adabins -- 9.3 State-of-the-art real-time facial detection -- 9.3.1 Introduction -- 9.4 Fruit classification -- 9.5 End notes -- References. |
| 520 ## - SUMMARY, ETC. | |
| Summary, etc. | This book provides a concise introduction to computer vision. It equips the reader with the tools needed to understand and engage with digital image processing, the algorithms of optical flow and the algorithms of object detection, using Python® software to show real, implemented applications in industry. A key resource for industry engineers with projects related to computer vision and researchers working at the intersection of AI and optics. |
| 588 ## - SOURCE OF DESCRIPTION NOTE | |
| Source of description note | Description based on publisher supplied metadata and other sources. |
| 590 ## - LOCAL NOTE (RLIN) | |
| Local note | Electronic reproduction. Ann Arbor, Michigan : ProQuest Ebook Central, 2024. Available via World Wide Web. Access may be limited to ProQuest Ebook Central affiliated libraries. |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name entry element | Computer vision. |
| 655 #4 - INDEX TERM--GENRE/FORM | |
| Genre/form data or focus term | Electronic books. |
| 700 1# - ADDED ENTRY--PERSONAL NAME | |
| Personal name | Chaparro-Romo, Héctor A. |
| 700 1# - ADDED ENTRY--PERSONAL NAME | |
| Personal name | Melendez-Montoya, Israel. |
| 776 08 - ADDITIONAL PHYSICAL FORM ENTRY | |
| Relationship information | Print version: |
| Main entry heading | González-Acuña, Rafael G |
| Title | Optics and Artificial Vision |
| Place, publisher, and date of publication | Bristol : Institute of Physics Publishing,c2021 |
| International Standard Book Number | 9780750337083 |
| 797 2# - LOCAL ADDED ENTRY--CORPORATE NAME (RLIN) | |
| Corporate name or jurisdiction name as entry element | ProQuest (Firm) |
| 830 #0 - SERIES ADDED ENTRY--UNIFORM TITLE | |
| Uniform title | IOP Series in Emerging Technologies in Optics and Photonics Series |
| 856 40 - ELECTRONIC LOCATION AND ACCESS | |
| Uniform Resource Identifier | <a href="https://ebookcentral.proquest.com/lib/orpp/detail.action?docID=31252986">https://ebookcentral.proquest.com/lib/orpp/detail.action?docID=31252986</a> |
| Public note | Click to View |
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