Computer Vision: Image Formation and Transformation

Related lectures (30)

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Covers Convolutional Neural Networks, standard architectures, training techniques, and adversarial examples in deep learning.

Explores the historical development of computer vision and inspires innovative project ideas.

Explores isometries, distinguishing between rotations and reflections, and the preservation of orientation in geometric transformations.

Covers the basics of computer vision, focusing on image processing techniques and feature detection.

Covers the techniques for recovering 3D shape information from 2D images using shading models and modern deep learning approaches.

Covers the basics of image processing using Fiji software, focusing on managing files and applying filters.

Explores the evolution of edge detection techniques, from Canny to deep learning insights.

Covers Gaussian random variables, affine transformations, and linear systems driven by Gaussian noise in multivariable control.

Explores the georeferencing of images, covering methodology steps, transformations, resampling, and practical application using QGIS software.

Explains axonometry, focusing on image of points and true magnitude of segments in 3D space.