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|a 9781681735559
|q electronic
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|a 10.2200/S00914ED2V01Y201904SPR017
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|a (MOCL)sdl201904sdl017
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|a (OCoLC)1101047072
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|a CaBNVSL
|b eng
|e rda
|c CaBNVSL
|d CaBNVSL
|
| 050 |
|
4 |
|a TK6680.3
|b .B737 2019eb
|
| 100 |
1 |
|
|a Braun, Henry,
|e author.
|0 http://id.loc.gov/authorities/names/n93063080.
|
| 245 |
1 |
0 |
|a Reconstruction-free compressive vision for surveillance applications /
|c Henry Braun, Pavan Turaga, Andreas Spanias, Sameeksha Katoch, Suren Jayasuriya, and Cihan Tepedelenlioglu.
|
| 264 |
|
1 |
|a [San Rafael, California] :
|b Morgan & Claypool,
|c [2019]
|
| 300 |
|
|
|a 1 PDF (xiii, 86 pages) :
|b illustrations (some color)
|
| 336 |
|
|
|a text
|b txt
|2 rdacontent.
|
| 337 |
|
|
|a computer
|b c
|2 rdamedia.
|
| 338 |
|
|
|a online resource
|b cr
|2 rdacarrier.
|
| 490 |
1 |
|
|a Synthesis lectures on signal processing,
|x 1932-1694 ;
|v #17.
|
| 500 |
|
|
|a Part of: Synthesis digital library of engineering and computer science.
|
| 504 |
|
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|a Includes bibliographical references (pages 67-81)
|
| 505 |
0 |
|
|a 1. Introduction -- 1.1. Targeted applications -- 1.2. Problem motivation -- 1.3. Organization.
|
| 505 |
8 |
|
|a 2. Compressed sensing fundamentals -- 2.1. Foundations of compressed sensing -- 2.2. Related convex problems -- 2.3. Sensors for compressive image and video capture -- 2.4. Non-convex reconstruction algorithms -- 2.5. Video reconstruction algorithms -- 2.6. Performance of CS reconstruction -- 2.7. Recovery of compressible signals -- 2.8. Deep learning for compressed sensing reconstruction -- 2.9. Summary.
|
| 505 |
8 |
|
|a 3. Computer vision and image processing for surveillance applications -- 3.1. Surveillance overview -- 3.2. Classification and detection algorithms -- 3.3. Tracking algorithms -- 3.4. Summary.
|
| 505 |
8 |
|
|a 4. Toward compressive vision -- 4.1. Smashed filter -- 4.2. Spatio-temporal smashed filters -- 4.3. Reconstruction-free compressive tracking algorithm -- 4.4. Classification -- 4.5. Compressive sensing for visual question answering.
|
| 505 |
8 |
|
|a 5. Conclusion -- 5.1. Final remarks and further reading.
|
| 520 |
3 |
|
|a Compressed sensing (CS) allows signals and images to be reliably inferred from undersampled measurements. Exploiting CS allows the creation of new types of high-performance sensors including infrared cameras and magnetic resonance imaging systems. Advances in computer vision and deep learning have enabled new applications of automated systems. In this book, we introduce reconstruction-free compressive vision, where image processing and computer vision algorithms are embedded directly in the compressive domain, without the need for first reconstructing the measurements into images or video. Reconstruction of CS images is computationally expensive and adds to system complexity. Therefore, reconstruction-free compressive vision is an appealing alternative particularly for power-aware systems and bandwidth-limited applications that do not have on-board post-processing computational capabilities. Engineers must balance maintaining algorithm performance while minimizing both the number of measurements needed and the computational requirements of the algorithms. Our study explores the intersection of compressed sensing and computer vision, with the focus on applications in surveillance and autonomous navigation. Other applications are also discussed at the end and a comprehensive list of references including survey papers are given for further reading.
|
| 588 |
|
|
|a Title from PDF title page (viewed on May 29, 2019)
|
| 650 |
|
0 |
|a Electronic surveillance.
|0 http://id.loc.gov/authorities/subjects/sh86006641.
|
| 650 |
|
0 |
|a Sensor networks.
|0 http://id.loc.gov/authorities/subjects/sh2003001274.
|
| 650 |
|
0 |
|a Computer vision.
|0 http://id.loc.gov/authorities/subjects/sh85029549.
|
| 650 |
|
0 |
|a Image processing.
|0 http://id.loc.gov/authorities/subjects/sh85064446.
|
| 700 |
1 |
|
|a Turaga, Pavan,
|e author.
|0 http://id.loc.gov/authorities/names/no2014059619.
|
| 700 |
1 |
|
|a Spanias, Andreas,
|e author.
|0 http://id.loc.gov/authorities/names/n2006001281
|1 http://isni.org/isni/0000000115718937.
|
| 700 |
1 |
|
|a Katoch, Sameeksha,
|e author.
|0 http://id.loc.gov/authorities/names/nb2019014705.
|
| 700 |
1 |
|
|a Jayasuriya, Suren,
|e author.
|0 http://id.loc.gov/authorities/names/nb2019014709.
|
| 700 |
1 |
|
|a Tepedelenlioğlu, Cihan,
|e author.
|0 http://id.loc.gov/authorities/names/no2010058965
|1 http://isni.org/isni/0000000077925715.
|
| 776 |
0 |
8 |
|i Print version:
|z 9781681735566
|z 9781681735542.
|
| 830 |
|
0 |
|a Synthesis lectures on signal processing ;
|v #17.
|0 http://id.loc.gov/authorities/names/no2007056717.
|
| 856 |
4 |
0 |
|u https://colorado.idm.oclc.org/login?url=https://doi.org/10.2200/S00914ED2V01Y201904SPR017
|z Full Text (via Morgan & Claypool)
|
| 907 |
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|a .b109906317
|b 06-01-20
|c 05-07-20
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|a MARS - RDA ENRICHED
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|p Can circulate
|a University of Colorado Boulder
|b Online
|c Online
|d Online
|e TK6680.3 .B737 2019eb
|h Library of Congress classification
|i web
|n 1
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