{"title":"Image segmentation by the modelisation of the biological visual systems","authors":"J. Girod, G. Martin, B. Heit, J. Brémont","doi":"10.1109/ICNN.1994.374564","DOIUrl":null,"url":null,"abstract":"The segmentation tool presented in this article takes advantage of orientation selection mechanisms which appear in the visual cortex, so that fine, well-situated edges are obtained in a grey-scale image. The search for the best spatial resolution limits our study to the central part of the fovea. The first part of this article deals with a schematic description of the path followed by visual information in the brain and, in particular, from the eye to the primary visual cortex. The model used accepts spatial grouping by the horizontal cells in Gaussian form, and takes advantage of the center-surround antagonism of the bipolar cells found on the retina. The model obtained, which is quite insensitive to noise, reconciles very well the different characteristics of the natural images without setting the parameters. The structure of operations employed in order to carry this out allows a real-time implementation on neural network or pipeline hardware to be envisaged.<<ETX>>","PeriodicalId":209128,"journal":{"name":"Proceedings of 1994 IEEE International Conference on Neural Networks (ICNN'94)","volume":"46 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1994-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"4","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of 1994 IEEE International Conference on Neural Networks (ICNN'94)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICNN.1994.374564","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 4
Abstract
The segmentation tool presented in this article takes advantage of orientation selection mechanisms which appear in the visual cortex, so that fine, well-situated edges are obtained in a grey-scale image. The search for the best spatial resolution limits our study to the central part of the fovea. The first part of this article deals with a schematic description of the path followed by visual information in the brain and, in particular, from the eye to the primary visual cortex. The model used accepts spatial grouping by the horizontal cells in Gaussian form, and takes advantage of the center-surround antagonism of the bipolar cells found on the retina. The model obtained, which is quite insensitive to noise, reconciles very well the different characteristics of the natural images without setting the parameters. The structure of operations employed in order to carry this out allows a real-time implementation on neural network or pipeline hardware to be envisaged.<>