Image Processing I: Segmentation and Thresholding

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Covers the basics of image processing and its applications in various fields.

Covers the basics of machine learning for physicists and chemists, focusing on image classification and dataset labeling.

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

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

Covers unsupervised learning focusing on clustering methods and the challenges faced in clustering algorithms like K-means and DBSCAN.

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

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

Covers clustering using K-means and LDA, PCA, K-means properties, Fisher LDA, and spectral clustering.

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

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