| 000 -LEADER |
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| fixed length control field |
05533nam a22007811i 4500 |
| 001 - CONTROL NUMBER |
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| control field |
8467550 |
| 003 - CONTROL NUMBER IDENTIFIER |
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| control field |
IEEE |
| 005 - DATE AND TIME OF LATEST TRANSACTION |
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| control field |
20200413152927.0 |
| 006 - FIXED-LENGTH DATA ELEMENTS--ADDITIONAL MATERIAL CHARACTERISTICS |
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| fixed length control field |
m eo d |
| 007 - PHYSICAL DESCRIPTION FIXED FIELD--GENERAL INFORMATION |
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| fixed length control field |
cr cn |||m|||a |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION |
|---|
| fixed length control field |
180926s2018 caua foab 000 0 eng d |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER |
|---|
| International Standard Book Number |
9781681730127 |
| Qualifying information |
ebook |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER |
|---|
| Canceled/invalid ISBN |
9781681732695 |
| Qualifying information |
hardcover |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER |
|---|
| Canceled/invalid ISBN |
9781681730110 |
| Qualifying information |
paperback |
| 024 7# - OTHER STANDARD IDENTIFIER |
|---|
| Standard number or code |
10.2200/S00819ED1V01Y201712COV014 |
| Source of number or code |
doi |
| 035 ## - SYSTEM CONTROL NUMBER |
|---|
| System control number |
(CaBNVSL)swl000408686 |
| 035 ## - SYSTEM CONTROL NUMBER |
|---|
| System control number |
(OCoLC)1054358004 |
| 040 ## - CATALOGING SOURCE |
|---|
| Original cataloging agency |
CaBNVSL |
| Language of cataloging |
eng |
| Description conventions |
rda |
| Transcribing agency |
CaBNVSL |
| Modifying agency |
CaBNVSL |
| 050 #4 - LIBRARY OF CONGRESS CALL NUMBER |
|---|
| Classification number |
TK7882.P3 |
| Item number |
D253 2018 |
| 082 04 - DEWEY DECIMAL CLASSIFICATION NUMBER |
|---|
| Classification number |
006.4 |
| Edition number |
23 |
| 100 1# - MAIN ENTRY--PERSONAL NAME |
|---|
| Personal name |
Dana, Kristin J., |
| Dates associated with a name |
1968- |
| Relator term |
author. |
| 245 10 - TITLE STATEMENT |
|---|
| Title |
Computational texture and patterns : |
| Remainder of title |
from textons to deep learning / |
| Statement of responsibility, etc. |
Kristin J. Dana. |
| 264 #1 - PRODUCTION, PUBLICATION, DISTRIBUTION, MANUFACTURE, AND COPYRIGHT NOTICE |
|---|
| Place of production, publication, distribution, manufacture |
[San Rafael, California] : |
| Name of producer, publisher, distributor, manufacturer |
Morgan & Claypool, |
| Date of production, publication, distribution, manufacture, or copyright notice |
2018. |
| 300 ## - PHYSICAL DESCRIPTION |
|---|
| Extent |
1 PDF (xiii, 99 pages) : |
| Other physical details |
illustrations. |
| 336 ## - CONTENT TYPE |
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| Content type term |
text |
| Source |
rdacontent |
| 337 ## - MEDIA TYPE |
|---|
| Media type term |
electronic |
| Source |
isbdmedia |
| 338 ## - CARRIER TYPE |
|---|
| Carrier type term |
online resource |
| Source |
rdacarrier |
| 490 1# - SERIES STATEMENT |
|---|
| Series statement |
Synthesis lectures on computer vision, |
| International Standard Serial Number |
2153-1064 ; |
| Volume/sequential designation |
# 14 |
| 538 ## - SYSTEM DETAILS NOTE |
|---|
| System details note |
Mode of access: World Wide Web. |
| 538 ## - SYSTEM DETAILS NOTE |
|---|
| System details note |
System requirements: Adobe Acrobat Reader. |
| 500 ## - GENERAL NOTE |
|---|
| General note |
Part of: Synthesis digital library of engineering and computer science. |
| 504 ## - BIBLIOGRAPHY, ETC. NOTE |
|---|
| Bibliography, etc. note |
Includes bibliographical references (pages 77-98). |
| 505 0# - FORMATTED CONTENTS NOTE |
|---|
| Formatted contents note |
1. Visual patterns and texture -- 1.1 Patterns in nature -- 1.2 Big data patterns -- 1.3 Temporal patterns -- 1.4 Organization -- |
| 505 8# - FORMATTED CONTENTS NOTE |
|---|
| Formatted contents note |
2. Textons in human and computer vision -- 2.1 Pre-attentive vision -- 2.2 Texton: the early definition -- 2.3 What are textons? Then and now -- |
| 505 8# - FORMATTED CONTENTS NOTE |
|---|
| Formatted contents note |
3. Texture recognition -- 3.1 Traditional methods of texture recognition -- 3.2 From textons to deep learning for recognition -- 3.3 Texture recognition with deep learning -- 3.4 Material recognition vs. texture recognition -- |
| 505 8# - FORMATTED CONTENTS NOTE |
|---|
| Formatted contents note |
4. Texture segmentation -- 4.1 Traditional methods of texture segmentation -- 4.1.1 Graph-based methods -- 4.1.2 Mean shift methods -- 4.1.3 Markov random fields -- 4.2 Segmentation with deep learning -- |
| 505 8# - FORMATTED CONTENTS NOTE |
|---|
| Formatted contents note |
5. Texture synthesis -- 5.1 Traditional methods for texture synthesis -- 5.2 Texture synthesis with deep learning -- |
| 505 8# - FORMATTED CONTENTS NOTE |
|---|
| Formatted contents note |
6. Texture style transfer -- 6.1 Traditional methods of style transfer -- 6.2 Texture style transfer with deep learning -- 6.3 Face style transfer -- |
| 505 8# - FORMATTED CONTENTS NOTE |
|---|
| Formatted contents note |
7. Return of the pyramids -- 7.1 Advantages of pyramid methods -- |
| 505 8# - FORMATTED CONTENTS NOTE |
|---|
| Formatted contents note |
8. Open issues in understanding visual patterns -- 8.1 Discovering unknown patterns -- 8.2 Detecting subtle change -- 8.3 Perceptual metrics -- |
| 505 8# - FORMATTED CONTENTS NOTE |
|---|
| Formatted contents note |
9. Applications for texture and patterns -- 9.1 Medical imaging and quantitative dermatology -- 9.2 Texture matching in industry -- 9.3 E-commerce -- 9.4 Textured solar panels -- 9.5 Road analysis for automated driving -- |
| 505 8# - FORMATTED CONTENTS NOTE |
|---|
| Formatted contents note |
10. Tools for mining patterns: cloud services and software libraries -- 10.1 Software libraries -- 10.2 Cloud services -- |
| 505 8# - FORMATTED CONTENTS NOTE |
|---|
| Formatted contents note |
A. A concise description of deep learning -- A.1 Multilayer perceptron -- A.2 Convolutional neural networks -- A.3 Alexnet, Dense-Net, Res-Nets, and all that -- |
| 505 8# - FORMATTED CONTENTS NOTE |
|---|
| Formatted contents note |
Bibliography -- Author's biography. |
| 506 ## - RESTRICTIONS ON ACCESS NOTE |
|---|
| Terms governing access |
Abstract freely available; full-text restricted to subscribers or individual document purchasers. |
| 510 0# - CITATION/REFERENCES NOTE |
|---|
| Name of source |
Compendex |
| 510 0# - CITATION/REFERENCES NOTE |
|---|
| Name of source |
INSPEC |
| 510 0# - CITATION/REFERENCES NOTE |
|---|
| Name of source |
Google scholar |
| 510 0# - CITATION/REFERENCES NOTE |
|---|
| Name of source |
Google book search |
| 520 3# - SUMMARY, ETC. |
|---|
| Summary, etc. |
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. |
| 530 ## - ADDITIONAL PHYSICAL FORM AVAILABLE NOTE |
|---|
| Additional physical form available note |
Also available in print. |
| 588 ## - SOURCE OF DESCRIPTION NOTE |
|---|
| Source of description note |
Title from PDF title page (viewed on September 26, 2018). |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name entry element |
Pattern recognition systems. |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name entry element |
Texture mapping. |
| 653 ## - INDEX TERM--UNCONTROLLED |
|---|
| Uncontrolled term |
texture |
| 653 ## - INDEX TERM--UNCONTROLLED |
|---|
| Uncontrolled term |
patterns |
| 653 ## - INDEX TERM--UNCONTROLLED |
|---|
| Uncontrolled term |
deep learning |
| 653 ## - INDEX TERM--UNCONTROLLED |
|---|
| Uncontrolled term |
machine learning |
| 653 ## - INDEX TERM--UNCONTROLLED |
|---|
| Uncontrolled term |
segmentation |
| 653 ## - INDEX TERM--UNCONTROLLED |
|---|
| Uncontrolled term |
synthesis |
| 653 ## - INDEX TERM--UNCONTROLLED |
|---|
| Uncontrolled term |
recognition |
| 653 ## - INDEX TERM--UNCONTROLLED |
|---|
| Uncontrolled term |
textons |
| 653 ## - INDEX TERM--UNCONTROLLED |
|---|
| Uncontrolled term |
style transfer |
| 776 08 - ADDITIONAL PHYSICAL FORM ENTRY |
|---|
| Relationship information |
Print version: |
| International Standard Book Number |
9781681730110 |
| -- |
9781681732695 |
| 830 #0 - SERIES ADDED ENTRY--UNIFORM TITLE |
|---|
| Uniform title |
Synthesis digital library of engineering and computer science. |
| 830 #0 - SERIES ADDED ENTRY--UNIFORM TITLE |
|---|
| Uniform title |
Synthesis lectures on computer vision ; |
| Volume/sequential designation |
# 14. |
| International Standard Serial Number |
2153-1064 |
| 856 42 - ELECTRONIC LOCATION AND ACCESS |
|---|
| Materials specified |
Abstract with links to resource |
| Uniform Resource Identifier |
https://ieeexplore.ieee.org/servlet/opac?bknumber=8467550 |
