{"title":"CNN图像处理中橘子的自动分类","authors":"P. Arena, L. Fortuna, G. Manganaro, S. Spina","doi":"10.1109/CNNA.1994.381631","DOIUrl":null,"url":null,"abstract":"A new image processing technique based on cellular neural networks for improving the automatic classification of fruits (in particular, oranges) is introduced. It allows the digitised orange images to be processed in order to highlight some peculiarities of the fruits. In this way the following classification step is greatly simplified and improved. Moreover, the real-time processing characteristic of CNNs is a very advantageous point over the traditional computing resources commonly used in this kind of processing. The proposed task is accomplished by the choice of suitable templates in a simple CNN model. These templates are described and some examples are reported.<<ETX>>","PeriodicalId":248898,"journal":{"name":"Proceedings of the Third IEEE International Workshop on Cellular Neural Networks and their Applications (CNNA-94)","volume":"20 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1994-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"6","resultStr":"{\"title\":\"CNN image processing for the automatic classification of oranges\",\"authors\":\"P. Arena, L. Fortuna, G. Manganaro, S. Spina\",\"doi\":\"10.1109/CNNA.1994.381631\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"A new image processing technique based on cellular neural networks for improving the automatic classification of fruits (in particular, oranges) is introduced. It allows the digitised orange images to be processed in order to highlight some peculiarities of the fruits. In this way the following classification step is greatly simplified and improved. Moreover, the real-time processing characteristic of CNNs is a very advantageous point over the traditional computing resources commonly used in this kind of processing. The proposed task is accomplished by the choice of suitable templates in a simple CNN model. These templates are described and some examples are reported.<<ETX>>\",\"PeriodicalId\":248898,\"journal\":{\"name\":\"Proceedings of the Third IEEE International Workshop on Cellular Neural Networks and their Applications (CNNA-94)\",\"volume\":\"20 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"1994-12-18\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"6\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of the Third IEEE International Workshop on Cellular Neural Networks and their Applications (CNNA-94)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/CNNA.1994.381631\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the Third IEEE International Workshop on Cellular Neural Networks and their Applications (CNNA-94)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/CNNA.1994.381631","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
CNN image processing for the automatic classification of oranges
A new image processing technique based on cellular neural networks for improving the automatic classification of fruits (in particular, oranges) is introduced. It allows the digitised orange images to be processed in order to highlight some peculiarities of the fruits. In this way the following classification step is greatly simplified and improved. Moreover, the real-time processing characteristic of CNNs is a very advantageous point over the traditional computing resources commonly used in this kind of processing. The proposed task is accomplished by the choice of suitable templates in a simple CNN model. These templates are described and some examples are reported.<>