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Related lectures (30)

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Automatic Vectorization: Summary

The lecture discusses the main types of automatic vectorization techniques.

Evaluating Machine Accuracy and Robustness on ImageNet

Explores the evaluation of machine and human accuracy and robustness on ImageNet, highlighting progress, challenges, and the need for improvement.

Automatic Vectorization Techniques

Explores automation techniques for vectorizing objects using polygonization, edge detection, segment detection, and automatic tracing.

Convolutional filters as inductive bias for images

Delves into convolutional filters as an inductive bias for images in neural networks, emphasizing independence to translation and local feature detectors.

Shape from Contours

Explores shape reconstruction from contours using edge-based stereo matching and visual hull techniques for accurate 3D reconstruction.

Greyscale, Color and Special Images

Explores Greyscale, color, and special image types, bit depths, RGB creation, and file formats.

Edge Detection: Basics

Covers the basics of edge detection, including edge duality, discontinuities, descriptors, gradients, and magnitude measurement.

Image Filtering: Classic Filters

Explores classic image filters like Moving Average, Gaussian Blur, Sobel filter, and Laplacian operator.

Reconstructing Color Images: Techniques and Applications

Covers techniques for reconstructing color images using optical detection and artificial intelligence to enhance image quality and reduce noise.

Image Processing Basics

Introduces image processing basics in Python, covering manipulation, grayscale conversion, edge detection, and convolution with kernels.