Name
Abstract | ||
|---|---|---|
In this paper we explore the utility of compressive sensing for object signature generation in the optical domain. In the data acquisition stage we use laser scanning to obtain a small (sub-Nyquist) number of points of an object's boundary. This is used to construct the signature, thereby enabling object identification, reconstruction, and, image data compression. We refer to this framework as compressive scanning of objects' signatures. The main contributions of the paper are the following: (1) we use this framework to replace parts of the digital processing with optical processing and present one possible implementation, (2) the use of compressive scanning reduces laser data obtained and maintains high reconstruction accuracy, and (3) we show that using compressive sensing can lead to a reduction in the amount of stored data without significantly affecting the utility of this data for image recognition and image compression. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1007/s11047-014-9460-7 | Natural Computing: an international journal |
Keywords | Field | DocType |
Digital signal processing,Optical signal processing,Compressive sensing,Shape representation,Object signature,Optical superComputing | Computer vision,Digital signal processing,Laser scanning,Computer science,Data acquisition,Laser,Artificial intelligence,Data compression,Image compression,Compressed sensing,Optical processing | Journal |
Volume | Issue | ISSN |
14 | 3 | 1567-7818 |
Citations | PageRank | References |
0 | 0.34 | 11 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jonathan I. Tamir | 1 | 40 | 12.04 |
| Dan E. Tamir | 2 | 79 | 13.26 |
| Wilhelmus J. Geerts | 3 | 5 | 1.50 |
| Shlomi Dolev | 4 | 2962 | 260.61 |