Alexander Rodríguez, Alicia Diaz-Zea, R. Flores, Medardo Delgado Paredes, D. Barrios-Aranibar, Raquel Patiño
{"title":"基于转换因子和调整因子确定权值的紫石竹法典编纂","authors":"Alexander Rodríguez, Alicia Diaz-Zea, R. Flores, Medardo Delgado Paredes, D. Barrios-Aranibar, Raquel Patiño","doi":"10.1109/SCCC.2011.20","DOIUrl":null,"url":null,"abstract":"The Codification of Argopecten Purpuratus is a process, where the Stem and Coral are classified by their weight in different codes. This process is done manually, therefore is linked to the subjectivity and the fatigue of people involved in the work. The use of computer vision is an alternative to automate this process. The present work proposes a method to classify the Argopecten Purpuratus based on determination of weights by conversion and adjustment factors. These factors use the area of the whole scallop and of the coral to make the estimation. Results of experiments show that the computer vision system achieved an overall acccuracy of 98%.","PeriodicalId":173639,"journal":{"name":"2011 30th International Conference of the Chilean Computer Science Society","volume":"522 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2011-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Argopecten Purpuratus Codification Based on Determination of Weight by Conversion and Adjustment Factors\",\"authors\":\"Alexander Rodríguez, Alicia Diaz-Zea, R. Flores, Medardo Delgado Paredes, D. Barrios-Aranibar, Raquel Patiño\",\"doi\":\"10.1109/SCCC.2011.20\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The Codification of Argopecten Purpuratus is a process, where the Stem and Coral are classified by their weight in different codes. This process is done manually, therefore is linked to the subjectivity and the fatigue of people involved in the work. The use of computer vision is an alternative to automate this process. The present work proposes a method to classify the Argopecten Purpuratus based on determination of weights by conversion and adjustment factors. These factors use the area of the whole scallop and of the coral to make the estimation. Results of experiments show that the computer vision system achieved an overall acccuracy of 98%.\",\"PeriodicalId\":173639,\"journal\":{\"name\":\"2011 30th International Conference of the Chilean Computer Science Society\",\"volume\":\"522 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2011-11-09\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2011 30th International Conference of the Chilean Computer Science Society\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/SCCC.2011.20\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2011 30th International Conference of the Chilean Computer Science Society","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/SCCC.2011.20","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Argopecten Purpuratus Codification Based on Determination of Weight by Conversion and Adjustment Factors
The Codification of Argopecten Purpuratus is a process, where the Stem and Coral are classified by their weight in different codes. This process is done manually, therefore is linked to the subjectivity and the fatigue of people involved in the work. The use of computer vision is an alternative to automate this process. The present work proposes a method to classify the Argopecten Purpuratus based on determination of weights by conversion and adjustment factors. These factors use the area of the whole scallop and of the coral to make the estimation. Results of experiments show that the computer vision system achieved an overall acccuracy of 98%.