DC Field | Value | Language |
---|---|---|
dc.contributor.author | Yun, Woojin | ko |
dc.contributor.author | Kyung, Chong-Min | ko |
dc.contributor.author | Kim, Young-Gyu | ko |
dc.contributor.author | LEE, YEONGMIN | ko |
dc.contributor.author | Lim, Jinyeon | ko |
dc.contributor.author | Choi, Won-Seok | ko |
dc.contributor.author | Muhammad Umar Karim, Khan | ko |
dc.contributor.author | Khan, Asim | ko |
dc.date.accessioned | 2017-12-05T01:33:16Z | - |
dc.date.available | 2017-12-05T01:33:16Z | - |
dc.date.created | 2017-11-27 | - |
dc.date.created | 2017-11-27 | - |
dc.date.created | 2017-11-27 | - |
dc.date.issued | 2017-09-18 | - |
dc.identifier.citation | 2017 IEEE International Conference on Image Processing(ICIP), pp.4392 - 4396 | - |
dc.identifier.issn | 1522-4880 | - |
dc.identifier.uri | http://hdl.handle.net/10203/227297 | - |
dc.description.abstract | Due to the increasing demand for 3D applications, development of novel depth-sensing cameras is being actively pursued. However, most of these cameras still face the challenge of high energy consumption and slow speed in the depth extraction process. This becomes a serious bottleneck in embedded implementations where real-time performance is required, constrained by power and area. This work proposes Offset Aperture (OA) camera, a new hardware architecture for fast, low-energy, and low-complexity depth extraction. Optimal implementations of pre-processing, cost-volume generation and cost-aggregation are presented. The whole depth-extraction pipeline has been implemented on a Field Programmable Gate Array (FPGA). Overall, a mere 2.8% of bad classification was achieved with the proposed system. Also, the proposed system can process 37 VGA frames per second while consuming 0.224 μJ/pixel. High accuracy, speed and low energy consumption of the proposed OA architecture make it suitable for embedded applications. | - |
dc.language | English | - |
dc.publisher | IEEE Signal Processing Society | - |
dc.title | OFFSET APERTURE BASED HARDWARE ARCHITECTURE FOR REAL-TIME DEPTH EXTRACTION | - |
dc.type | Conference | - |
dc.identifier.wosid | 000428410704105 | - |
dc.identifier.scopusid | 2-s2.0-85045301956 | - |
dc.type.rims | CONF | - |
dc.citation.beginningpage | 4392 | - |
dc.citation.endingpage | 4396 | - |
dc.citation.publicationname | 2017 IEEE International Conference on Image Processing(ICIP) | - |
dc.identifier.conferencecountry | CC | - |
dc.identifier.conferencelocation | China National Convention Center, Beijing | - |
dc.identifier.doi | 10.1109/ICIP.2017.8297112 | - |
dc.contributor.localauthor | Kyung, Chong-Min | - |
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