Abstract
This chapter describes several commonly used algorithms in computer vision. Algorithms discussed include adaptive Gaussian thresholding and mixture modeling for edge detection; cross correlation template matching for shape detection; the Viola Jones model for face detection; Gaussian mixture modeling for motion detection; and the histogram of oriented gradients feature descriptor and support vector machines (including directed acyclic graph multi-class support vector machines) for image classification. Several of these techniques also have wider application in other areas of machine learning.
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This research is supported in part by the National Research Foundation of South Africa (UNIQUE GRANT NO: 105670).
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Ghaziasgar, M., Bagula, A., Thron, C. (2020). Computer Vision Algorithms for Image Segmentation, Motion Detection, and Classification. In: Subair, S., Thron, C. (eds) Implementations and Applications of Machine Learning. Studies in Computational Intelligence, vol 782. Springer, Cham. https://doi.org/10.1007/978-3-030-37830-1_5
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