From Intensities To Objects: Summary

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Image Processing I: Segmentation and Thresholding

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

Quantitative Imaging for Civil Engineers

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

K-Means Clustering: Basics and Applications

Introduces K-Means Clustering, a simple yet effective algorithm for grouping data points into clusters.

Color in Depth: Projections, Reslicing Summary

Covers color models, projections, and reslicing in image analysis for life sciences.

Machine Learning Fundamentals

Covers the fundamental concepts of machine learning, including classification, algorithms, optimization, supervised learning, reinforcement learning, and various tasks like image recognition and text generation.

Particle Characterization: Methods and Applications

Discusses methods for characterizing particle shapes and sizes, focusing on measurement challenges and the importance of standardized protocols.

K-means Clustering: Initialization and Image Segmentation

Explores k-means clustering, emphasizing initialization and image segmentation.

Image Processing Fundamentals

Covers the basics of image processing for microscopy, including acquiring, correcting defects, enhancing images, and extracting information.

Image Classification: Decision Trees & Random Forests

Explores image classification using decision trees and random forests to reduce variance and improve model robustness.

K-means Clustering: Lloyd's Algorithm and RGB Space

Explains K-means clustering with Lloyd's algorithm and RGB space for color segmentation.