Texture Segmentation Using Localized Spatial Filtering
Spatial filters based on two-dimensional Gabor functions are applied to the image segmentation problem using textural differences for discrimination. In order to provide class separability, the textural content of a scene must have spatial variations which exhibit characteristic differences in frequ...
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| Format: | Text |
| Language: | English |
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1990
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| Online Access: | http://www.dtic.mil/docs/citations/ADA218064 http://oai.dtic.mil/oai/oai?&verb=getRecord&metadataPrefix=html&identifier=ADA218064 |
| Summary: | Spatial filters based on two-dimensional Gabor functions are applied to the image segmentation problem using textural differences for discrimination. In order to provide class separability, the textural content of a scene must have spatial variations which exhibit characteristic differences in frequency and/or directional bandwidths. This idea stems from discoveries in vision research that the Gabor functions model effectively the architecture of the neural receptive fields in the striate visual cortex and in the belief that such functions can play an important role in the analytical study of machine vision, pattern recognition and image processing. This new technique in image segmentation does not required burdensome machine data processing as compared with other techniques based on pixel classification. In this paper the technique is applied to some SAR images of the open ocean surface and of some ice fields. The results are very encouraging. Original contains color plates: All DTIC and NTIS reproductions will be in black and white. |
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