Project

Development of computational methods in volumes: Segmentation, Modeling and classification of organic structures in rocks

Goal: identication and classification of organic structures in rock volumes using imaging processing and machine learning

Methods: Machine Learning, Image Analysis, Image Segmentation, Filtering Techniques

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Luís Eduardo Ramos de Carvalho
added a research item
Computational analysis applicability to paleontological images ranges from the study of the evolution of animals, plants and microorganisms to the habitat simulation of living beings from a specific epoch. It can also be applied in several niches, e.g. oil exploration, where several factors can be analyzed in order to minimize costs related to oil extraction. One specific factor is the characterization of the environment to be explored. This analysis can occur in several ways: use of probes, samples extraction, correlation with logs of other drilling wells and so on. During the samples extraction phase, the Computed Tomography (CT) is of extreme importance, since it preserves the sample and makes it available for several analyses. Based on 3D images generated by CT, analyses and simulations can be performed, and processes currently performed manually and exhaustively, can be automated. In this work, we propose and validate a method for fully automated microfossil identification and segmentation. A pipeline is proposed that begins with scanning and ends with the microfossil segmentation process. For the microfossil segmentation, a Deep Learning approach was developed, which resulted in a high rate of correct microfossil segmentation (98% IOU). The validation was performed both through an automated quantitative analysis and visual inspection. The study was performed on a limited dataset, but the results provide evidence that our approach has potential to be generalized to other carbonatic rock substrates. To the extent of the authors' knowledge, this paper presents the first fully annotated MicroCT acquired microfossils dataset made publicly available.
Aldo Von Wangenheim
added a research item
The applicability of computational analysis to paleontological images ranges from the study of the animals, plants and evolution of microorganisms to the simulation of the habitat of living beings of a given epoch. It also can be applied in several niches, such as oil exploration, where there are several factors to be analyzed in order to minimize the expenses related to the oil extraction process. One factor is the characterization of the environment to be explored. This analysis can occur in several ways: use of probes, extraction of samples for petrophysical components evaluation, the correlation with logs of other drilling wells and so on. In the samples extraction part the Computed Tomography (CT) is of importance because it preserves the sample and makes it available for several analyzes. Based on 3D images generated by CT, several analyzes and simulations can be performed and processes, currently performed manually and exhaustively, can be automated. In this work we propose and validate a method for fully automated microfossil identification and extraction. A pipeline is proposed that begins in the scanning process and ends in an identification process. For the identification a Deep Learning approach was developed, which resulted in a high rate of correct microfossil identification (98% of Intersection Over Union). The validation was performed both through an automated quantitative analysis based upon ground truths generated by specialists in the micropaleontology field and visual inspection by these specialists. We also present the first fully annotated MicroCT-acquired publicly available microfossils dataset.
Luís Eduardo Ramos de Carvalho
added a research item
In this paper, we present a systematic literature review concerning 3D object recognition and classification. We cover articles published between 2006 and 2016 available in three scientific databases (ScienceDirect, IEEE Xplore and ACM), using the methodology for systematic review proposed by Kitchenham. Based on this methodology, we used tags and exclusion criteria to select papers about the topic under study. After the works selection, we applied a categorization process aiming to group similar object representation types, analyzing the steps applied for object recognition, the tests and evaluation performed and the databases used. Lastly, we compressed all the obtained information in a general overview and presented future prospects for the area. Link for the publication: https://link.springer.com/epdf/10.1007/s10044-019-00804-4?author_access_token=paE7wTbqwKN7oCwVliHwLve4RwlQNchNByi7wbcMAY7uL2tJzq0UXA0O13kX7wvxz98EQWbRDi2uT7G5KxVe0WzCAoagCbJhmkFlrCPdZIPfyyYkaSt_0zAEiJJc2cojH9AajAmYQ5BT1LV4EonJMg%3D%3D
Aldo Von Wangenheim
added a research item
A ´area de reconhecimento/classificac¸ ˜ao de objetos 3D ´e uma ´area que nos ´ultimos anos teve um crescimento impulsionado pela maior disponibilidade de bases de dados com dados de objetos 3D e da popularizac¸ ˜ao de sensores para captura de objetos em um ambiente. A aplicabilidade destes m´etodos v˜ao desde o dom´ınio da rob´otica, voltada para movimentac¸ ˜ao de robˆos em ambientes e manipulac¸ ˜ao, por brac¸os rob´oticos, de objetos at´e o dom´ınio de seguranc¸a, onde utilizam-se as t´ecnicas de reconhecimento/classificac¸ ˜ao de objetos 3D para detectar poss´ıveis objetos proibidos em um avi˜ao. Visando ter um melhor conhecimento sobre o assunto, identificando m´etodos de classificac¸ ˜ao/reconhecimento, e, por consequˆencia, poss´ıveis formas de descric¸ ˜ao de objetos, elaborou-se uma revis˜ao sistem´atica da literatura. Este documento tem como prop´osito detalhar todos os procedimentos relacionados para a revis˜ao sistem´atica da literatura, para o t´opico de classificac¸ ˜ao/reconhecimento de objetos 3D, para o per´ıodo de 2006-2016, e realizar uma an´alise do estado da arte sobre o assunto, com o foco nas poss´ıveis aplicac¸ ˜oes destes m´etodos em outras ´areas. Este relat´orio t´ecnico est´a divido nas seguintes sec¸ ˜oes. A sec¸ ˜ao de metodologia, define qual foram os passos executados para a realizac¸ ˜ao deste trabalho. A sec¸ ˜ao de descric¸ ˜ao da pesquisa, detalha os parˆametros de pesquisa utilizados bem como crit´erios de selec¸ ˜ao empregados na selec¸ ˜ao dos trabalhos a serem analisados. A sec¸ ˜ao de trabalhos realizados, categoriza e detalha os trabalhos selecionados para an´alise, com foco na forma de descric¸ ˜ao e reconhecimento/classificac¸ ˜ao dos objetos. A an´alise e discuss˜ao sobre os trabalhos selecionados ´e realizada na sec¸ ão de discussão sobre os trabalhos analisados.
Luís Eduardo Ramos de Carvalho
added a research item
This paper presents a systematic literature review concerning 3D segmentation algorithms for computerized tomographic imaging. This analysis covers articles published in the range 2006—March 2018 found in four scientific databases (Science Direct, IEEEXplore, ACM, and PubMed), using the methodology for systematic review proposed by Kitchenham. We present the analyzed segmentation methods categorized according to its application, algorithmic strategy, validation, and use of prior knowledge, as well as its general conceptual description. Additionally, we present a general overview, discussions, and further prospects for the 3D segmentation methods applied for tomographic images.
Luís Eduardo Ramos de Carvalho
added a research item
This technical report presents a systematic literature review concerning 3D segmentation algorithms applied to computerized tomographic imaging. This review covers works published between 2006 and july of 2016 found in 3 scientific databases (Science Direct, IEEEXplore and ACM), using the methodology for systematic review proposed by Kitchenham. We present the segmentation methods categorized according to its application, algorithmic strategy, validation, use of prior knowledge, as well as its general conceptual description. Additionally, we present a general overview, discussions and further prospects for the 3D segmentation methods applied for tomographic images.
Luís Eduardo Ramos de Carvalho
added a research item
A systematic literature review concerning 3D object recognition and classification is presented in this Techinical Report. This analysis covers articles published between 2006-2016 available in three scientific databases (Science Direct, IEEEXplore and ACM), using the methodology for systematic review proposed by Kitchenham. Based on this methodology, we used tags and an exclusion criteria to select papers about the topic on study. After the selection, we applied a categorization process aiming to group similar object representation types, analyzing the steps applied for object recognition, the test performed and the databases used. Lastly, we performed a general overview and presented future perspectives for the area.
Luís Eduardo Ramos de Carvalho
added a project goal
identication and classification of organic structures in rock volumes using imaging processing and machine learning