From Intensities To Objects: Summary

Related lectures (30)

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Covers basic to advanced morphological operations for image analysis workflows.

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

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

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

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

Explores denoising signals with Gaussian mixture models and EM algorithm, EMG signal analysis, and image segmentation using Markovian models.

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

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

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

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