Title:
A Multi-Stage Neural Network for Automatic Target Detection
A Multi-Stage Neural Network for Automatic Target Detection
| dc.contributor.author | Howard, Ayanna M. | en_US |
| dc.contributor.author | Padgett, Curtis | en_US |
| dc.contributor.author | Liebe, Carl Christian | en_US |
| dc.contributor.corporatename | Jet Propulsion Laboratory (U.S.) | en_US |
| dc.contributor.corporatename | Georgia Institute of Technology. Center for Robotics and Intelligent Machines | en_US |
| dc.date.accessioned | 2011-04-05T18:06:15Z | |
| dc.date.available | 2011-04-05T18:06:15Z | |
| dc.date.issued | 1998-05 | |
| dc.description | ©1998 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works. | en_US |
| dc.description | Presented at the 1998 International Joint Conference on Neural Networks (IJCNN) Anchorage, Alaska, May 1998. | en_US |
| dc.description | DOI: 10.1109/IJCNN.1998.682268 | en_US |
| dc.description.abstract | Automatic Target Recognition (ATR) involves processing two-dimensional images for detecting, classifying, and tracking targets. The first stage in ATR is the detection process. This involves discrimination between target and non-target objects in a scene. In this paper, we shall discuss a novel approach which addresses the target detection process. This method extracts relevant object features utilizing principal component analysis. These extracted features are then presented to a multi-stage neural network which allows an overall increase in detection rate, while decreasing the false positive alarm rate. We shall discuss the techniques involved and present some detection results that have been implemented on the multi-stage neural network. | en_US |
| dc.identifier.citation | A. Howard, C. Padgett, C. Liebe, “A Multi-Stage Neural Network for Automatic Target Detection,” The 1998 IEEE International Joint Conference on Neural Networks (IJCNN) Anchorage, Alaska, May 1998, Vol. 1, 231-236. | en_US |
| dc.identifier.doi | 10.1109/IJCNN.1998.682268 | |
| dc.identifier.isbn | 0-7803-4859-1 | |
| dc.identifier.uri | http://hdl.handle.net/1853/38381 | |
| dc.language.iso | en_US | en_US |
| dc.publisher | Georgia Institute of Technology | en_US |
| dc.publisher.original | Institute of Electrical and Electronics Engineers | en_US |
| dc.subject | Target recognition | en_US |
| dc.subject | Neural networks | en_US |
| dc.subject | Detection | en_US |
| dc.title | A Multi-Stage Neural Network for Automatic Target Detection | en_US |
| dc.type | Text | |
| dc.type.genre | Proceedings | |
| dspace.entity.type | Publication | |
| local.contributor.author | Howard, Ayanna M. | |
| local.contributor.corporatename | School of Civil and Environmental Engineering | |
| local.contributor.corporatename | Institute for Robotics and Intelligent Machines (IRIM) | |
| relation.isAuthorOfPublication | 6d77e175-105c-4b0b-9548-31f20e60e20a | |
| relation.isOrgUnitOfPublication | 88639fad-d3ae-4867-9e7a-7c9e6d2ecc7c | |
| relation.isOrgUnitOfPublication | 66259949-abfd-45c2-9dcc-5a6f2c013bcf |
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