The selective attention for identification model (SAIM): Simulating visual search in natural colour images
We recently presented a computational model of object recognition and attention: the Selective Attention for Identification model (SAIM) [1,2,3,4,5,6,7]. SAIM was developed to model normal attention and attentional disorders by implementing translation-invariant object recognition in multiple object...
| Main Authors: | , , , |
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| Format: | Journal article |
| Language: | English |
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2007
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| _version_ | 1797101361862541312 |
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| author | Heinke, D Backhaus, A Sun, Y Humphreys, G |
| author_facet | Heinke, D Backhaus, A Sun, Y Humphreys, G |
| author_sort | Heinke, D |
| collection | OXFORD |
| description | We recently presented a computational model of object recognition and attention: the Selective Attention for Identification model (SAIM) [1,2,3,4,5,6,7]. SAIM was developed to model normal attention and attentional disorders by implementing translation-invariant object recognition in multiple object scenes. SAIM can simulate a wide range of experimental evidence on normal and disordered attention. In its earlier form, SAIM could only process black and white images. The present paper tackles this important shortcoming by extending SAIM with a biologically plausible feature extraction, using Gabor filters and coding colour information in HSV-colour space. With this extension SAIM proved able to select and recognize objects in natural multiple-object colour scenes. Moreover, this new version still mimicked human data on visual search tasks. These results stem from the competitive parallel interactions that characterize processing in SAIM. © Springer-Verlag Berlin Heidelberg 2007. |
| first_indexed | 2024-03-07T05:50:48Z |
| format | Journal article |
| id | oxford-uuid:e8d176fd-f96e-492e-b88d-d2e8b45de1e8 |
| institution | University of Oxford |
| language | English |
| last_indexed | 2024-03-07T05:50:48Z |
| publishDate | 2007 |
| record_format | dspace |
| spelling | oxford-uuid:e8d176fd-f96e-492e-b88d-d2e8b45de1e82022-03-27T10:49:32ZThe selective attention for identification model (SAIM): Simulating visual search in natural colour imagesJournal articlehttp://purl.org/coar/resource_type/c_dcae04bcuuid:e8d176fd-f96e-492e-b88d-d2e8b45de1e8EnglishSymplectic Elements at Oxford2007Heinke, DBackhaus, ASun, YHumphreys, GWe recently presented a computational model of object recognition and attention: the Selective Attention for Identification model (SAIM) [1,2,3,4,5,6,7]. SAIM was developed to model normal attention and attentional disorders by implementing translation-invariant object recognition in multiple object scenes. SAIM can simulate a wide range of experimental evidence on normal and disordered attention. In its earlier form, SAIM could only process black and white images. The present paper tackles this important shortcoming by extending SAIM with a biologically plausible feature extraction, using Gabor filters and coding colour information in HSV-colour space. With this extension SAIM proved able to select and recognize objects in natural multiple-object colour scenes. Moreover, this new version still mimicked human data on visual search tasks. These results stem from the competitive parallel interactions that characterize processing in SAIM. © Springer-Verlag Berlin Heidelberg 2007. |
| spellingShingle | Heinke, D Backhaus, A Sun, Y Humphreys, G The selective attention for identification model (SAIM): Simulating visual search in natural colour images |
| title | The selective attention for identification model (SAIM): Simulating visual search in natural colour images |
| title_full | The selective attention for identification model (SAIM): Simulating visual search in natural colour images |
| title_fullStr | The selective attention for identification model (SAIM): Simulating visual search in natural colour images |
| title_full_unstemmed | The selective attention for identification model (SAIM): Simulating visual search in natural colour images |
| title_short | The selective attention for identification model (SAIM): Simulating visual search in natural colour images |
| title_sort | selective attention for identification model saim simulating visual search in natural colour images |
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