Lectures related to Canny edge detector

Edge and Contour

Covers edge and contour detection in images, including gradient-based methods, Laplacian operator, and more complex methods.

Image Processing: Neighborhood Averaging & Gaussian Smoothing

Explores neighborhood averaging, Gaussian smoothing, median filtering, contrast enhancement, and edge detection in image processing.

Image Filtering: Classic Filters

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

Image Processing I: Edge Detection and Texture Analysis

Covers edge detection, texture analysis, Canny's algorithm, filterbanks, and segmentation techniques.

Image Processing I: Correlation Measures and Matched Filtering

Explores correlation measures, matched filtering, and feature extraction in image processing.

Deep Learning: Edge Detection and Neural Networks

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

Directional Image Analysis: Steerable Filters

Explores directional image analysis and the implementation of steerable filters for edge and ridge detection.

Image Processing Basics

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

Computer Vision Basics: Image Processing and Feature Detection

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

Edge Detection: Deep Learning Insights

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

Edge Detection: Basics and Techniques

Introduces the basics of edge detection, including measuring contrast, gradient images, Fourier interpretation, Gaussian functions, Canny edge detector, and industrial applications.

Seam Carving: Energy Minimization

Introduces seam carving for image resizing through energy minimization techniques.

Edge Detection: Basics

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

Image Processing in Python: Manipulation and Convolution Techniques

Introduces image processing techniques in Python, focusing on manipulation and convolution operations using NumPy and Pillow.

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.

Automatic Vectorization: Principles and Applications

Covers the potential and limits of automatic vectorization techniques for digitizing objects from scanned documents or images.

Topology: Polyhedral Products and Edge Detection

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

Digital Sustainability: Challenges and Solutions

Explores digital sustainability, multi-scale computing, and energy-efficient cloud systems.

Directional Image Analysis and Processing

Delves into the mathematical foundations and importance of directional cues in image processing, exploring computational challenges and selectivity to orientation.

Image Processing I: Linear Operators and Fourier Transform

Introduces linear operators, Fourier transform, and their applications in image processing.