Name
Abstract | ||
|---|---|---|
Compressive sensing is a new framework for signal acquisition, compression, and processing. Of specific interest are two-dimensional signals such as images where an optical unit performs the acquisition and compression (i.e., compressive sensing or compressive imaging). The signal reconstruction and processing can be done by optical signal processing and/or digital signal processing. In this paper we review the theoretical basis of compressive sensing, present an optical implementation of image acquisition, and introduce a new application of compressive sensing where the actual signals used in the compressive sensing process are image object-signature (an object-signature is a specific representation of an object). We detail the application of compressive sensing to image object-signature and show the potential of compressive sensing to compress the data through analysis of several methods for obtaining signature and evaluation of the rate/distortions results of different compression methods including compressive sensing applied to object-signature. |
Year | DOI | Venue |
|---|---|---|
2010 | 10.1007/978-3-642-22494-2_8 | OSC |
Keywords | Field | DocType |
signal reconstruction,different compression method,image object-signature,signal acquisition,compressive imaging,two-dimensional signal,image acquisition,digital signal processing,optical signal processing,actual signal,compressive sensing,compressive sampling | Compression (physics),Signal processing,Computer vision,Digital signal processing,Signal acquisition,Computer science,Artificial intelligence,Compressed sensing,Signal reconstruction | Conference |
Volume | ISSN | Citations |
6748 | 0302-9743 | 2 |
PageRank | References | Authors |
0.40 | 13 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Dan E. Tamir | 1 | 79 | 13.26 |
| Natan T. Shaked | 2 | 6 | 1.87 |
| Wilhelmus J. Geerts | 3 | 5 | 1.50 |
| Shlomi Dolev | 4 | 2962 | 260.61 |