Texture Analysis: Statistical and Structural Approaches

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Introduces Convolutional Neural Networks, explaining their architecture, training process, and applications in semantic segmentation tasks.

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Covers image smoothing, noise reduction, and segmentation techniques using filters and transforms.

Covers the basics of Convolutional Neural Networks, including training optimization, layer structure, and potential pitfalls of summary statistics.

Explores the historical development of computer vision and inspires innovative project ideas.

Explores correlation measures, matched filtering, and feature extraction in image processing.

Discusses how learning sparse features can lead to overfitting in neural networks despite empirical evidence of generalization.

Covers the conclusions on deep learning and an introduction to statistical learning theory.

Introduces artificial neural networks, CNNs, and dimensionality reduction using PCA and autoencoders.

Covers advanced transformer architectures in deep learning, focusing on Swin, HUBERT, and Flamingo models for multimodal applications.