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Computer Vision

This module explores computer vision, a field of Artificial Intelligence that trains computers to interpret and understand the visual world. It covers techniques such as image recognition, object detection, and deep learning models that enable applications like automated inspection, surveillance, and autonomous driving.

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Hartley, Richard, and Andrew Zisserman. Multiple View Geometry in Computer Vision. 2nd ed. Cambridge, UK ; New York: Cambridge University Press, 2003.

Gonzalez, Rafael C., and Richard E. Woods. Digital Image Processing. 3rd ed. Upper Saddle River, N.J: Prentice Hall, 2008.

Sonka, Milan, Vaclav Hlavac, and Roger Boyle. Image Processing, Analysis, and Machine Vision. Fourth edition. Stamford, CT, USA: Cengage Learning, 2015.

Szeliski, Richard. Computer Vision: Algorithms and Applications. 2nd ed. Texts in Computer Science. Cham: Springer International Publishing, 2022.

https://doi.org/10.1007/978-3-030-34372-9

Khan, Salman, Hossein Rahmani, Syed Afaq Ali Shah, and Mohammed Bennamoun. A Guide to Convolutional Neural Networks for Computer Vision. Synthesis Lectures on Computer Vision. Cham: Springer International Publishing, 2018.

https://doi.org/10.1007/978-3-031-01821-3

Goodfellow, Ian, Yoshua Bengio, and Aaron Courville. Deep Learning. Adaptive Computation and Machine Learning Series. The MIT Press, 2016.

Forsyth, David, and Jean Ponce. Computer Vision: A Modern Approach. 2nd ed. Boston: Pearson, 2012.

Bishop, Christopher M. Pattern Recognition and Machine Learning. Information Science and Statistics. New York: Springer, 2006.