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Optics and artificial vision / Rafael G. Gonz�alez-Acu�ana, H�ector A. Chaparro-Romo, Israel Melendez-Montoya.

By: Gonz�alez-Acu�ana, Rafael G [author.].
Contributor(s): Chaparro-Romo, H�ector A [author.] | Melendez-Montoya, Israel [author.] | Institute of Physics (Great Britain) [publisher.].
Material type: materialTypeLabelBookSeries: IOP (Series)Release 21: ; IOP series in emerging technologies in optics and photonics: ; IOP ebooks2021 collection: Publisher: Bristol [England] (Temple Circus, Temple Way, Bristol BS1 6HG, UK) : IOP Publishing, [2021]Description: 1 online resource (various pagings) : illustrations (some color).Content type: text Media type: electronic Carrier type: online resourceISBN: 9780750337076; 9780750337069.Subject(s): Computer vision | Optical physics | Optics and photonicsAdditional physical formats: Print version:: No titleDDC classification: 006.37 Online resources: Click here to access online Also available in print.
Contents:
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 -- 5.5. Haralick features -- 5.6. Local binary patterns -- 5.7. Keypoint detectors -- 5.8. Local invariant descriptors -- 5.9. Binary descriptors -- 5.10. End notes
6. Neural networks -- 6.1. Introduction -- 6.2. Neural networks in a nutshell -- 6.3. Single perceptron learning -- 6.4. Multilayer perceptrons -- 6.5. Convolutional neural networks -- 6.6. Metrics -- 6.7. CNN architectures -- 6.8. Transfer learning -- 6.9. End notes
7. Optical character recognition -- 7.1. Introduction -- 7.2. Problems in classical OCR -- 7.3. The basic scheme of a classical OCR algorithm -- 7.4. Classical OCR using machine learning -- 7.5. Modern OCR with deep learning -- 7.6. OCR with Tesseract -- 7.7. End notes
8. Facial recognition -- 8.1. Introduction to facial recognition -- 8.2. Local binary patterns for facial recognition -- 8.3. The eigenfaces algorithm -- 8.4. Example using the CALTECH faces dataset -- 8.5. A LBP face recognizer for your own face -- 8.6. Deep learning facial recognition -- 8.7. End notes
9. Artificial vision case studies -- 9.1. Measuring the camera-object distance -- 9.2. Single image depth estimation -- 9.3. State-of-the-art real-time facial detection -- 9.4. Fruit classification -- 9.5. End notes.
Abstract: 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 with projects related to computer vision, as well as a good reference text for academics, students and other researchers working at the intersection of artificial intelligence and optics.
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"Version: 202109"--Title page verso.

Includes bibliographical references.

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 -- 5.5. Haralick features -- 5.6. Local binary patterns -- 5.7. Keypoint detectors -- 5.8. Local invariant descriptors -- 5.9. Binary descriptors -- 5.10. End notes

6. Neural networks -- 6.1. Introduction -- 6.2. Neural networks in a nutshell -- 6.3. Single perceptron learning -- 6.4. Multilayer perceptrons -- 6.5. Convolutional neural networks -- 6.6. Metrics -- 6.7. CNN architectures -- 6.8. Transfer learning -- 6.9. End notes

7. Optical character recognition -- 7.1. Introduction -- 7.2. Problems in classical OCR -- 7.3. The basic scheme of a classical OCR algorithm -- 7.4. Classical OCR using machine learning -- 7.5. Modern OCR with deep learning -- 7.6. OCR with Tesseract -- 7.7. End notes

8. Facial recognition -- 8.1. Introduction to facial recognition -- 8.2. Local binary patterns for facial recognition -- 8.3. The eigenfaces algorithm -- 8.4. Example using the CALTECH faces dataset -- 8.5. A LBP face recognizer for your own face -- 8.6. Deep learning facial recognition -- 8.7. End notes

9. Artificial vision case studies -- 9.1. Measuring the camera-object distance -- 9.2. Single image depth estimation -- 9.3. State-of-the-art real-time facial detection -- 9.4. Fruit classification -- 9.5. End notes.

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 with projects related to computer vision, as well as a good reference text for academics, students and other researchers working at the intersection of artificial intelligence and optics.

Optical engineers, data scientists, AI programmers, academics in optics and in machine learning.

Also available in print.

Mode of access: World Wide Web.

System requirements: Adobe Acrobat Reader, EPUB reader, or Kindle reader.

Rafael G. Gonz�alez-Acu�ana studied a master's degree in optomechatronics at the Optics Research Center, A.C. He is currently studying his PhD at the Tecnol�ogico de Monterrey. Rafael has been awarded the 2019 Optical Design and Engineering Scholarship by SPIE and is the co-author of two IOP books: Analytical lens design and Stigmatic Optics. H�ector A. Chaparro Romo is an electronic engineer from Universidad Autonoma Metropolitana. He is the co-author of the IOP books: Analytical lens design and Stigmatic Optics. Israel Melendez-Montoya is a physicist from Universidad Aut�onoma de Nuevo Le�on with master studies in optics from Tecnol�ogico de Monterrey. Israel has experimented in developing applications connecting optics and computer vision in multiple industrial projects.

Title from PDF title page (viewed on October 9, 2021).

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