Reconstruction-free compressive vision for surveillance applications /
By: Braun, Henry [author.].
Contributor(s): Turaga, Pavan [author.] | Spanias, Andreas [author.] | Katoch, Sameeksha [author.] | Jayasuriya, Suren [author.] | Tepedelenlioglu, Cihan [author.].
Material type:
BookSeries: Synthesis digital library of engineering and computer science: ; Synthesis lectures on signal processing: #17.Publisher: [San Rafael, California] : Morgan & Claypool, [2019]Description: 1 PDF (xiii, 86 pages) : illustrations (some color).Content type: text Media type: electronic Carrier type: online resourceISBN: 9781681735559.Subject(s): Electronic surveillance | Sensor networks | Computer vision | Image processing | compressed sensing | sparse representations | track-before-detect | deep learning | surveillanceDDC classification: 621.38928 Online resources: Abstract with links to full text | Abstract with links to resource Also available in print.| Item type | Current location | Call number | Status | Date due | Barcode | Item holds |
|---|---|---|---|---|---|---|
E books
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PK Kelkar Library, IIT Kanpur | Available | EBKE908 |
Mode of access: World Wide Web.
System requirements: Adobe Acrobat Reader.
Part of: Synthesis digital library of engineering and computer science.
Includes bibliographical references (pages 67-81).
1. Introduction -- 1.1. Targeted applications -- 1.2. Problem motivation -- 1.3. Organization
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
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
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
5. Conclusion -- 5.1. Final remarks and further reading.
Abstract freely available; full-text restricted to subscribers or individual document purchasers.
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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.
Also available in print.
Title from PDF title page (viewed on May 29, 2019).
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