Lectures related to Morphological Operations

Segmentation: Theory and Algorithms

Covers the theory and algorithms behind image segmentation, focusing on region identification and evaluation.

From Intensities To Objects: Summary

Summarizes image segmentation, morphological filters, and converting pixels to objects.

Image Processing I: Local Normalization and Morphological Operators

Introduces local normalization and morphological operators for shape analysis in image processing.

Image Processing I: Morphological Filtering and Operators

Explores dilation, erosion, opening, closing, and graylevel morphology in image processing.

Texture Analysis: Statistical and Structural Approaches

Discusses texture analysis in images, focusing on statistical and structural properties, segmentation techniques, and machine learning applications for texture classification.

Image Processing I: Segmentation and Thresholding

Explores image segmentation, thresholding techniques, texture segmentation, and connected-component labeling in image processing.

Image Processing I: Composition, Pooling, Continuity, and Denoising

Explores composition, pooling, continuity, denoising, and popular CNN architectures for image segmentation in image processing.

Drone Imaging Analysis

Explores drone imaging analysis using Meshroom software for civil engineering applications.

Segmentation Techniques

Explores segmentation techniques in image analysis, including thresholding, clustering, region growing, and machine learning.

Shape from Shading: Recovering 3D Information from 2D Images

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

Image processing: Basics and 3D Reconstruction

Explores image processing in 2D and 3D, covering ideal imaging conditions, histogram analysis, tools, 3D reconstruction steps, and visualization.

Deep Learning: Convolutional Neural Networks

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

Deep Learning: Edge Detection and Neural Networks

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

From Intensities to Objects Introduction

Introduces segmentation techniques, from intensities to objects and measurements generation.

Land Use Mapping in the Alps

Explores soil sealing impact, land use statistics, image segmentation, and random forest classification for sustainable land management.

Detecting Objects: Independent Channels

Emphasizes the need for independent channels when detecting objects in images.

Quantitative Imaging for Civil Engineers

Introduces quantitative imaging concepts for civil engineers, covering resolution, optics, image quality, and 3D measurements.

Image Processing in Frequency Space

Explores Fourier Transform, frequency filtering, segmentation, and particle size estimation using image analysis techniques.

A Localization Tale: Practical Course by BIOP

Covers practical aspects of using Fiji for everyday tasks in biological imaging.

Proper Feature Size: Image Analysis

Delves into choosing a proper feature size for image analysis in life sciences, presenting a Rule Of Thumb for defining object size in pixels.