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020 _a9780750337076
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020 _a9780750337069
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020 _z9780750337083
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024 7 _a10.1088/978-0-7503-3707-6
_2doi
035 _a(CaBNVSL)thg00082643
040 _aCaBNVSL
_beng
_erda
_cCaBNVSL
_dCaBNVSL
050 4 _aTA1634
_b.G666 2021eb
072 7 _aPHJ
_2bicssc
072 7 _aSCI053000
_2bisacsh
082 0 4 _a006.37
_223
100 1 _aGonz�alez-Acu�ana, Rafael G.,
_eauthor.
_970541
245 1 0 _aOptics and artificial vision /
_cRafael G. Gonz�alez-Acu�ana, H�ector A. Chaparro-Romo, Israel Melendez-Montoya.
264 1 _aBristol [England] (Temple Circus, Temple Way, Bristol BS1 6HG, UK) :
_bIOP Publishing,
_c[2021]
300 _a1 online resource (various pagings) :
_billustrations (some color).
336 _atext
_2rdacontent
337 _aelectronic
_2isbdmedia
338 _aonline resource
_2rdacarrier
490 1 _a[IOP release $release]
490 1 _aIOP series in emerging technologies in optics and photonics
490 1 _aIOP ebooks. [2021 collection]
500 _a"Version: 202109"--Title page verso.
504 _aIncludes bibliographical references.
505 0 _a1. 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
505 8 _a2. 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
505 8 _a3. 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
505 8 _a4. 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
505 8 _a5. 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
505 8 _a6. 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
505 8 _a7. 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
505 8 _a8. 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
505 8 _a9. 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.
520 3 _aThis 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.
521 _aOptical engineers, data scientists, AI programmers, academics in optics and in machine learning.
530 _aAlso available in print.
538 _aMode of access: World Wide Web.
538 _aSystem requirements: Adobe Acrobat Reader, EPUB reader, or Kindle reader.
545 _aRafael 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.
588 0 _aTitle from PDF title page (viewed on October 9, 2021).
650 0 _aComputer vision.
_970542
650 7 _aOptical physics.
_2bicssc
_970543
650 7 _aOptics and photonics.
_2bisacsh
_918815
700 1 _aChaparro-Romo, H�ector A.,
_eauthor.
_970544
700 1 _aMelendez-Montoya, Israel,
_eauthor.
_970545
710 2 _aInstitute of Physics (Great Britain),
_epublisher.
_911622
776 0 8 _iPrint version:
_z9780750337052
_z9780750337083
830 0 _aIOP (Series).
_pRelease 21.
_970546
830 0 _aIOP series in emerging technologies in optics and photonics.
_970547
830 0 _aIOP ebooks.
_p2021 collection.
_970548
856 4 0 _uhttps://iopscience.iop.org/book/978-0-7503-3707-6
942 _cEBK
999 _c82859
_d82859