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Lecture
Introduction to ImageJ
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Related lectures (31)
Computer Vision: Image Formation and Transformation
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Provides an overview of image formation and transformation principles in computer vision.
Segmentation: Theory and Algorithms
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Covers the theory and algorithms behind image segmentation, focusing on region identification and evaluation.
Shape from Shading: Recovering 3D Information from 2D Images
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Covers the techniques for recovering 3D shape information from 2D images using shading models and modern deep learning approaches.
Image Processing Fundamentals
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Covers scientific image processing fundamentals, software practices, and ethical considerations in image processing.
Texture Analysis: Statistical and Structural Approaches
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Discusses texture analysis in images, focusing on statistical and structural properties, segmentation techniques, and machine learning applications for texture classification.
Image Processing in Frequency Space
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Explores Fourier Transform, frequency filtering, segmentation, and particle size estimation using image analysis techniques.
Image Classification: Decision Trees & Random Forests
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Explores image classification using decision trees and random forests to reduce variance and improve model robustness.
Gaussian Mixture Models: Data Classification
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Explores denoising signals with Gaussian mixture models and EM algorithm, EMG signal analysis, and image segmentation using Markovian models.
Image Processing I
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Covers the basics of image processing and its applications in various fields.
Image Processing I: Composition, Pooling, Continuity, and Denoising
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Explores composition, pooling, continuity, denoising, and popular CNN architectures for image segmentation in image processing.
Deep Learning: Edge Detection and Neural Networks
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Discusses edge detection techniques and the evolution of deep learning in neural networks.
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