{"title":"利用混合特征进行微观颗粒分割和矿物识别分类的框架","authors":"Ghazanfar Latif, Kévin Bouchard, Julien Maitre, Arnaud Back, Léo Paul Bédard","doi":"10.1007/s12145-024-01478-1","DOIUrl":null,"url":null,"abstract":"<p>Mineral grain recognition is an extremely important task in many fields, especially in mineral exploration, when trying to identify locations where precious minerals can possibly be found. The usual manual method would be to collect samples; a specialized individual using expensive equipment would manually identify and then count the grain minerals in the sample. This is a tedious task that is time-consuming and expensive. It is also limited because small portions of areas can be surveyed; even then, it might require extremely long periods. In addition, this process is still prone to human errors. Developing an automatic system to identify, recognize, and count grain minerals in samples from images would allow for more precise results than the time required by humans. In addition, such systems can be fitted on robots that collect samples, take images of the samples, and then proceed with the automated recognition and counting algorithm without human intervention. Vast amounts of land can be surveyed in this way. This paper proposes a modified approach for microscopic grain mineral recognition and classification using hybrid features and ensemble algorithms from images. The enhanced approach also included a modified segmentation approach, which enhanced the results. For 10 classes of microscopic mineral grains, using the modified approach and the ensemble algorithm resulted in an average accuracy of 84.01%. For 8 classes, the average reported accuracy is 94.93% using the Boosting ensemble learning with the C4.5 classifier. The results obtained outperform similar methods reported in the extant literature.</p>","PeriodicalId":49318,"journal":{"name":"Earth Science Informatics","volume":null,"pages":null},"PeriodicalIF":2.7000,"publicationDate":"2024-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"A framework for microscopic grains segmentation and Classification for Minerals Recognition using hybrid features\",\"authors\":\"Ghazanfar Latif, Kévin Bouchard, Julien Maitre, Arnaud Back, Léo Paul Bédard\",\"doi\":\"10.1007/s12145-024-01478-1\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p>Mineral grain recognition is an extremely important task in many fields, especially in mineral exploration, when trying to identify locations where precious minerals can possibly be found. The usual manual method would be to collect samples; a specialized individual using expensive equipment would manually identify and then count the grain minerals in the sample. This is a tedious task that is time-consuming and expensive. It is also limited because small portions of areas can be surveyed; even then, it might require extremely long periods. In addition, this process is still prone to human errors. Developing an automatic system to identify, recognize, and count grain minerals in samples from images would allow for more precise results than the time required by humans. In addition, such systems can be fitted on robots that collect samples, take images of the samples, and then proceed with the automated recognition and counting algorithm without human intervention. Vast amounts of land can be surveyed in this way. This paper proposes a modified approach for microscopic grain mineral recognition and classification using hybrid features and ensemble algorithms from images. The enhanced approach also included a modified segmentation approach, which enhanced the results. For 10 classes of microscopic mineral grains, using the modified approach and the ensemble algorithm resulted in an average accuracy of 84.01%. For 8 classes, the average reported accuracy is 94.93% using the Boosting ensemble learning with the C4.5 classifier. The results obtained outperform similar methods reported in the extant literature.</p>\",\"PeriodicalId\":49318,\"journal\":{\"name\":\"Earth Science Informatics\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":2.7000,\"publicationDate\":\"2024-09-12\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Earth Science Informatics\",\"FirstCategoryId\":\"89\",\"ListUrlMain\":\"https://doi.org/10.1007/s12145-024-01478-1\",\"RegionNum\":4,\"RegionCategory\":\"地球科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Earth Science Informatics","FirstCategoryId":"89","ListUrlMain":"https://doi.org/10.1007/s12145-024-01478-1","RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS","Score":null,"Total":0}
A framework for microscopic grains segmentation and Classification for Minerals Recognition using hybrid features
Mineral grain recognition is an extremely important task in many fields, especially in mineral exploration, when trying to identify locations where precious minerals can possibly be found. The usual manual method would be to collect samples; a specialized individual using expensive equipment would manually identify and then count the grain minerals in the sample. This is a tedious task that is time-consuming and expensive. It is also limited because small portions of areas can be surveyed; even then, it might require extremely long periods. In addition, this process is still prone to human errors. Developing an automatic system to identify, recognize, and count grain minerals in samples from images would allow for more precise results than the time required by humans. In addition, such systems can be fitted on robots that collect samples, take images of the samples, and then proceed with the automated recognition and counting algorithm without human intervention. Vast amounts of land can be surveyed in this way. This paper proposes a modified approach for microscopic grain mineral recognition and classification using hybrid features and ensemble algorithms from images. The enhanced approach also included a modified segmentation approach, which enhanced the results. For 10 classes of microscopic mineral grains, using the modified approach and the ensemble algorithm resulted in an average accuracy of 84.01%. For 8 classes, the average reported accuracy is 94.93% using the Boosting ensemble learning with the C4.5 classifier. The results obtained outperform similar methods reported in the extant literature.
期刊介绍:
The Earth Science Informatics [ESIN] journal aims at rapid publication of high-quality, current, cutting-edge, and provocative scientific work in the area of Earth Science Informatics as it relates to Earth systems science and space science. This includes articles on the application of formal and computational methods, computational Earth science, spatial and temporal analyses, and all aspects of computer applications to the acquisition, storage, processing, interchange, and visualization of data and information about the materials, properties, processes, features, and phenomena that occur at all scales and locations in the Earth system’s five components (atmosphere, hydrosphere, geosphere, biosphere, cryosphere) and in space (see "About this journal" for more detail). The quarterly journal publishes research, methodology, and software articles, as well as editorials, comments, and book and software reviews. Review articles of relevant findings, topics, and methodologies are also considered.