Computational Texture and Patterns : From Textons to Deep Learning Paperback / softback
by Kristin J. Dana
Part of the Synthesis Lectures on Computer Vision series
Paperback / softback
Description
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.
Information
-
Item not Available
- Format:Paperback / softback
- Pages:113 pages
- Publisher:Morgan & Claypool Publishers
- Publication Date:13/09/2018
- Category:
- ISBN:9781681730110
Other Formats
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Information
-
Item not Available
- Format:Paperback / softback
- Pages:113 pages
- Publisher:Morgan & Claypool Publishers
- Publication Date:13/09/2018
- Category:
- ISBN:9781681730110
