Lectures related to Edge and Contour

Image Processing: Neighborhood Averaging & Gaussian Smoothing

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

Computer Vision Basics: Image Processing and Feature Detection

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

Image Filtering: Classic Filters

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

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.

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.

Directional Image Analysis: Steerable Filters

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

Edge Detection: Deep Learning Insights

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

Image Processing Techniques

Covers image processing techniques including noise addition, filtering, and image enhancement using various filters and tools.

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: Correlation Measures and Matched Filtering

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

Image Processing: Enhancement and Analysis

Explores image processing techniques such as contrast manipulation, enhancement, and sharpening using Fiji software.

Deep Learning: Edge Detection and Neural Networks

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

Image Processing I: Discrete Histogram Processing and Spatial Operators

Explores discrete histogram processing and spatial operators for image manipulation and noise reduction.

Image Processing I: Edge Detection and Texture Analysis

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

Image Processing Basics

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

Image Processing: Smoothing and Filtering

Covers image smoothing, noise reduction, and segmentation techniques using filters and transforms.

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.

Image Processing Fundamentals

Covers scientific image processing fundamentals, software practices, and ethical considerations in image processing.