Image Processing I

Related lectures (29)

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

Delves into neural signals, GLM, ANOVA, brain mapping, connectivity, and multivariate methods.

Discusses the electronics of CCD cameras, focusing on output amplifiers and charge conversion processes.

Covers neural signals, brain enthusiasm, neuroimaging, and statistical analysis in neuroimaging studies.

Explores topology concepts and edge detection in computer vision, highlighting the significance of contours and gradients in image analysis.

Explores the use of optics in machine learning, focusing on large-scale random matrix multiplication through multiple scattering of light.

Covers Fourier transformation and its application in optical detection using the concept of the Fourier house.

Explores the optimization of neuroprosthetic systems, including sensory feedback restoration and neural stimulation strategies.

Explores LabVIEW essentials, troubleshooting common issues, managing cache, and data visualization techniques.

Discusses edge detection techniques and the evolution of deep learning in neural networks.