{"title":"基于块化HLAC的木材识别新方法","authors":"Lingran Ma, Hang-jun Wang","doi":"10.1109/ICNC.2012.6234674","DOIUrl":null,"url":null,"abstract":"This paper propose a new method for wood recognition based on texture analysis. At first, wood texture images are divided into several blocks in our method. Secondly, wood features are extracted from these blocked grey-scale images using different mask, which is knows as higher-order local autocorrelation (HLAC). Finally, Support Vector Machine (SVM) was used to verify the performance of the method. Experiments carried on the wood texture database demonstrate that our method outperforms the original HLAC method.","PeriodicalId":404981,"journal":{"name":"2012 8th International Conference on Natural Computation","volume":"63 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2012-05-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":"{\"title\":\"A new method for wood recognition based on blocked HLAC\",\"authors\":\"Lingran Ma, Hang-jun Wang\",\"doi\":\"10.1109/ICNC.2012.6234674\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This paper propose a new method for wood recognition based on texture analysis. At first, wood texture images are divided into several blocks in our method. Secondly, wood features are extracted from these blocked grey-scale images using different mask, which is knows as higher-order local autocorrelation (HLAC). Finally, Support Vector Machine (SVM) was used to verify the performance of the method. Experiments carried on the wood texture database demonstrate that our method outperforms the original HLAC method.\",\"PeriodicalId\":404981,\"journal\":{\"name\":\"2012 8th International Conference on Natural Computation\",\"volume\":\"63 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2012-05-29\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"3\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2012 8th International Conference on Natural Computation\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICNC.2012.6234674\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2012 8th International Conference on Natural Computation","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICNC.2012.6234674","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
A new method for wood recognition based on blocked HLAC
This paper propose a new method for wood recognition based on texture analysis. At first, wood texture images are divided into several blocks in our method. Secondly, wood features are extracted from these blocked grey-scale images using different mask, which is knows as higher-order local autocorrelation (HLAC). Finally, Support Vector Machine (SVM) was used to verify the performance of the method. Experiments carried on the wood texture database demonstrate that our method outperforms the original HLAC method.