Šimon Grác, Peter Beno, F. Duchoň, Michal Malý, Martin Dekan
{"title":"利用机器学习方法汇总多个空间视图进行物体分类","authors":"Šimon Grác, Peter Beno, F. Duchoň, Michal Malý, Martin Dekan","doi":"10.2478/jee-2024-0017","DOIUrl":null,"url":null,"abstract":"\n The article proposes a solution for object classification using multiple views generated from 3D data rendering and convolutional neural networks. For presentation purposes and easier verification of the solution, an application was developed to create views of 3D objects, classify them using the selected CNN, and evaluate the performance of the CNN. The evaluation is based on metrics and characteristics described in the article. Seven testing objects were used to verify the proposed solution; five CNNs were tested for each.","PeriodicalId":508697,"journal":{"name":"Journal of Electrical Engineering","volume":"162 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2024-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Object classification with aggregating multiple spatial views using a machine-learning approach\",\"authors\":\"Šimon Grác, Peter Beno, F. Duchoň, Michal Malý, Martin Dekan\",\"doi\":\"10.2478/jee-2024-0017\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"\\n The article proposes a solution for object classification using multiple views generated from 3D data rendering and convolutional neural networks. For presentation purposes and easier verification of the solution, an application was developed to create views of 3D objects, classify them using the selected CNN, and evaluate the performance of the CNN. The evaluation is based on metrics and characteristics described in the article. Seven testing objects were used to verify the proposed solution; five CNNs were tested for each.\",\"PeriodicalId\":508697,\"journal\":{\"name\":\"Journal of Electrical Engineering\",\"volume\":\"162 1\",\"pages\":\"\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2024-04-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Electrical Engineering\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.2478/jee-2024-0017\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Electrical Engineering","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.2478/jee-2024-0017","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Object classification with aggregating multiple spatial views using a machine-learning approach
The article proposes a solution for object classification using multiple views generated from 3D data rendering and convolutional neural networks. For presentation purposes and easier verification of the solution, an application was developed to create views of 3D objects, classify them using the selected CNN, and evaluate the performance of the CNN. The evaluation is based on metrics and characteristics described in the article. Seven testing objects were used to verify the proposed solution; five CNNs were tested for each.