Optics and artificial vision

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This book provides a concise introduction to computer vision for optical researchers and scientists. Building from the optical foundations of image processing and the science behind camera sensors, Optics and Artificial Vision 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 Pythonª software to show real, implemented applications in industry. Ideal for industry engineers w…

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Title Statements

Full title :Optics and artificial vision / Rafael G. González-Acuäna, Héctor A. Chaparro-Romo, Israel Melendez-Montoya.

Summary

Edition Statements

Publication Date:2021

Publication Statements

Publisher(s):IOP Publishing
Place of Publication:Bristol [England] (Temple Circus, Temple Way, Bristol BS1 6HG, UK)
Language:eng
Publication Date:2021

Notes

"Version: 202109"--Title page verso.

The Physical Object

Description:1 online resource (various pagings) : : illustrations (some color).

Edition Identifiers

ISBN:0750337079, 9780750337069, 0750337060, 9780750337076
DOI:10.1088/978-0-7503-3707-6
oclc:on1280926008
LC Classification:TA1634
Dewey Decimal:006.37

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

2. Introduction to computer vision -- 2.1. Loading and saving images -- 2.2. Image basics -- 2.3. Colour spaces -- 2.4. Basic image processing -- 2.5. Resizing images -- 2.6. Kernels and morphological operations -- 2.7. Blurring -- 2.8. Thresholding -- 2.9. Gradients and edge detection -- 2.10. Histograms -- 2.11. End notes

3. Optical flow -- 3.1. Introduction -- 3.2. 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 -- 3.6. End notes

4. Object detection algorithms -- 4.1. Object detection -- 4.2. Sliding windows and image pyramids -- 4.3. The histogram of oriented gradients descriptor -- 4.4. Support vector machine -- 4.5. End notes

5. Image descriptors -- 5.1. Introduction to image descriptors -- 5.2. Basic statistics -- 5.3. Hu moments -- 5.4. Zernike moments…