Optics and artificial vision /

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 dig...

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Detalles Bibliográficos
Clasificación:Libro Electrónico
Autores principales: González-Acuäna, Rafael G. (Autor), Chaparro-Romo, Héctor A. (Autor), Melendez-Montoya, Israel (Autor)
Formato: Electrónico eBook
Idioma:Inglés
Publicado: Bristol [England] (Temple Circus, Temple Way, Bristol BS1 6HG, UK) : IOP Publishing, [2021]
Colección:IOP (Series). Release 21.
IOP series in emerging technologies in optics and photonics.
IOP ebooks. 2021 collection.
Temas:
Computer vision.
Optical physics.
Optics and photonics.
Acceso en línea:Texto completo
Tabla de Contenidos:
  • 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.

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