| 1. Identity statement | |
| Reference Type | Conference Paper (Conference Proceedings) |
| Site | sibgrapi.sid.inpe.br |
| Holder Code | ibi 8JMKD3MGPEW34M/46T9EHH |
| Identifier | 6qtX3pFwXQZeBBx/vRThU |
| Repository | sid.inpe.br/banon/2002/10.24.10.41 |
| Last Update | 2002:09.03.03.00.00 (UTC) administrator |
| Metadata Repository | sid.inpe.br/banon/2002/10.24.10.41.18 |
| Metadata Last Update | 2022:06.14.00.11.51 (UTC) administrator |
| DOI | 10.1109/SIBGRA.2002.1167129 |
| Citation Key | MartinsGuimFons:2002:TeFeNe |
| Title | Texture feature neural classifier for remote sensing image retrieval systems  |
| Year | 2002 |
| Access Date | 2024, Apr. 27 |
| Number of Files | 1 |
| Size | 863 KiB |
| 2. Context | |
| Author | 1 Martins, Mauricio Pozzobon 2 Guimaraes, Lamartine N. Frutuoso 3 Fonseca, Leila Maria Garcia |
| Editor | Gonçalves, Luiz Marcos Garcia Musse, Soraia Raupp Comba, João Luiz Dihl Giraldi, Gilson Dreux, Marcelo |
| Conference Name | Brazilian Symposium on Computer Graphics and Image Processing, 15 (SIBGRAPI) |
| Conference Location | Fortaleza, CE, Brazil |
| Date | 10-10 Oct. 2002 |
| Publisher | IEEE Computer Society |
| Publisher City | Los Alamitos |
| Book Title | Proceedings |
| Tertiary Type | Full Paper |
| Organization | SBC - Brazilian Computer Society |
| History (UTC) | 2008-07-17 14:10:47 :: administrator -> banon :: 2008-08-26 15:21:22 :: banon -> administrator :: 2009-08-13 20:36:41 :: administrator -> banon :: 2010-08-28 20:00:07 :: banon -> administrator :: 2022-06-14 00:11:51 :: administrator -> :: 2002 |
| 3. Content and structure | |
| Is the master or a copy? | is the master |
| Content Stage | completed |
| Transferable | 1 |
| Version Type | finaldraft |
| Abstract | Texture information is useful for image data browsing and retrieval. The goal of this paper is to present a texture classification system for remote sensing images addressed to the administration of great collections of those images. The proposed classifier is a hybrid system composed by an unsupervised neural network and a supervised one. Starting from a small portion of the image (pattern) the system should recognize the most similar class to the pattern in a database as well as to identify the images that contain similar patterns. The texture feature vectors used to characterize the patterns are obtained from the images processed by a bank of Gabor Filters. Some experimental results using textures of the Brodatz album, multi-spectral and radar images have presented here. |
| Arrangement 1 | urlib.net > SDLA > Fonds > SIBGRAPI 2002 > Texture feature neural... |
| Arrangement 2 | urlib.net > SDLA > Fonds > Full Index > Texture feature neural... |
| doc Directory Content | access |
| source Directory Content | there are no files |
| agreement Directory Content | there are no files |
| 4. Conditions of access and use | |
| data URL | http://urlib.net/ibi/6qtX3pFwXQZeBBx/vRThU |
| zipped data URL | http://urlib.net/zip/6qtX3pFwXQZeBBx/vRThU |
| Language | en |
| Target File | 81.pdf |
| User Group | administrator |
| Visibility | shown |
| 5. Allied materials | |
| Next Higher Units | 8JMKD3MGPEW34M/46QCSHP 8JMKD3MGPEW34M/4742MCS |
| Citing Item List | sid.inpe.br/sibgrapi/2022/05.01.04.11 5 |
| Host Collection | sid.inpe.br/banon/2001/03.30.15.38 |
| 6. Notes | |
| Notes | The conference was held in Fortaleza, CE, Brazil, from October 7 to 10. |
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