Computational Texture and Patterns
From Textons to Deep Learning
Springer International Publishing
ISBN 978-3-031-01823-7
Standardpreis
Bibliografische Daten
eBook. PDF. Weiches DRM (Wasserzeichen)
2022
XIII, 99 p..
In englischer Sprache
Umfang: 99 S.
Verlag: Springer International Publishing
ISBN: 978-3-031-01823-7
Weiterführende bibliografische Daten
Das Werk ist Teil der Reihe: Synthesis Lectures on Computer Vision
Produktbeschreibung
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.
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