Computational texture and patterns: From textons to deep learning

✍ Scribed by Dana, Kristin J

• Publisher: Morgan & Claypool Publishers
• Year: 2018
• Tongue: English
• Leaves: 115
• Series: Synthesis lectures on computer vision 14
• Category: Library

✦ Synopsis

Visual pattern analysis is a fundamental tool in mining data for knowledge. Computational representations for patterns and texture allow us to summarize, store, compare, and label in order to learn about the physical world. Our ability to capture visual imagery with cameras and sensors has resulted in vast amounts of raw data, but using this information effectively in a task-specific manner requires sophisticated computational representations. We enumerate specific desirable traits for these representations: (1) intraclass invariance-to support recognition; (2) illumination and geometric invariance for robustness to imaging conditions; (3) support for prediction and synthesis to use the model to infer continuation of the pattern; (4) support for change detection to detect anomalies and perturbations; and (5) support for physics-based interpretation to infer system properties from appearance. In recent years, computer vision has undergone a metamorphosis with classic algorithms adapting to new trends in deep learning. This text provides a tour of algorithm evolution including pattern recognition, segmentation and synthesis. We consider the general relevance and prominence of visual pattern analysis and applications that rely on computational models.

✦ Table of Contents

Preface......Page 13
Acknowledgments......Page 15
Visual Patterns and Texture......Page 17
Patterns in Nature......Page 19
Big Data Patterns......Page 20
Temporal Patterns......Page 23
Organization......Page 25
Texton: The Early Definition......Page 27
What are Textons? Then and Now......Page 31
Traditional Methods of Texture Recognition......Page 33
Multilayer Perceptron......Page 0
Texture Recognition with Deep Learning......Page 36
Traditional Methods of Texture Segmentation......Page 41
Markov Random Fields......Page 45
Segmentation with Deep Learning......Page 47
Traditional Methods for Texture Synthesis......Page 55
Texture Synthesis with Deep Learning......Page 58
Texture Style Transfer with Deep Learning......Page 63
Face Style Transfer......Page 64
Detecting Subtle Change......Page 69
Perceptual Metrics......Page 70
Medical Imaging and Quantitative Dermatology......Page 71
Textured Solar Panels......Page 72
Road Analysis for Automated Driving......Page 73
Cloud Services......Page 76
A Concise Description of Deep Learning......Page 79
Convolutional Neural Networks......Page 83
Alexnet, Dense-Net, Res-Nets, and All That......Page 89
Bibliography......Page 93
Author's Biography......Page 115
Blank Page......Page 2