Doutorado em Ciência da Computação
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Navegando Doutorado em Ciência da Computação por Por Orientador "Soares, Fabrízzio Alphonsus Alves de Melo Nunes"
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Item Análise multirresolução de imagens gigapixel para detecção de faces e pedestres(Universidade Federal de Goiás, 2023-09-27) Ferreira, Cristiane Bastos Rocha; Pedrini, Hélio; http://lattes.cnpq.br/9600140904712115; Soares, Fabrízzio Alphonsus Alves de Melo Nunes; http://lattes.cnpq.br/7206645857721831; Soares, Fabrízzio Alphonsus Alves de Melo Nunes; Pedrini, Helio; Santos, Edimilson Batista dos; Borges, Díbio Leandro; Fernandes, Deborah Silva AlvesGigapixel images, also known as gigaimages, can be formed by merging a sequence of individual images obtained from a scene scanning process. Such images can be understood as a mosaic construction based on a large number of high resolution digital images. A gigapixel image provides a powerful way to observe minimal details that are very far from the observer, allowing the development of research in many areas such as pedestrian detection, surveillance, security, and so forth. As this image category has a high volume of data captured in a sequential way, its generation is associated with many problems caused by the process of generating and analyzing them, thus, applying conventional algorithms designed for non-gigapixel images in a direct way can become unfeasible in this context. Thus, this work proposes a method for scanning, manipulating and analyzing multiresolution Gigapixel images for pedestrian and face identification applications using traditional algorithms. This approach is analyzed using both Gigapixel images with low and high density of people and faces, presenting promising results.Item Reconhecimento de padrões em imagens radiográficas de tórax: apoiando o diagnóstico de doenças pulmonares infecciosas(Universidade Federal de Goiás, 2023-09-29) Fonseca, Afonso Ueslei da; Soares, Fabrízzio Alphonsus Alves de Melo Nunes; http://lattes.cnpq.br/7206645857721831; Soares, Fabrízzio Alphonsus Alves de Melo Nunes; Laureano, Gustavo Teodoro; Pedrini, Hélio; Rabahi, Marcelo Fouad; Salvini, Rogerio LopesPattern Recognition (PR) is a field of computer science that aims to develop techniques and algorithms capable of identifying regularities in complex data, enabling intelligent systems to perform complicated tasks with precision. In the context of diseases, PR plays a crucial role in diagnosis and detection, revealing patterns hidden from human eyes, assisting doctors in making decisions and identifying correlations. Infectious pulmonary diseases (IPD), such as pneumonia, tuberculosis, and COVID-19, challenge global public health, causing thousands of deaths annually, affecting healthcare systems, and demanding substantial financial resources. Diagnosing them can be challenging due to the vagueness of symptoms, similarities with other conditions, and subjectivity in clinical assessment. For instance, chest X-ray (CXR) examinations are a tedious and specialized process with significant variation among observers, leading to failures and delays in diagnosis and treatment, especially in underdeveloped countries with a scarcity of radiologists. In this thesis, we investigate PR and Artificial Intelligence (AI) techniques to support the diagnosis of IPID in CXRs. We follow the guidelines of the World Health Organization (WHO) to support the goals of the 2030 Agenda, which includes combating infectious diseases. The research questions involve selecting the best techniques, acquiring data, and creating intelligent models. As objectives, we propose low-cost, high-efficiency, and effective PR and AI methods that range from preprocessing to supporting the diagnosis of IPD in CXRs. The results so far align with the state of the art, and we believe they can contribute to the development of computer-assisted IPD diagnostic systems.Item Detecção automática e avaliação de linhas de plantio de cana-de-açúcar em imagens aéreas(Universidade Federal de Goiás, 2021-12-09) Rocha, Bruno Moraes; Pedrini, Hélio; http://lattes.cnpq.br/9600140904712115 Nome completo do 2º coorientador(a): E-mail: Nomes completos; Soares, Fabrízzio Alphonsus Alves de Melo Nunes; http://lattes.cnpq.br/7206645857721831; Soares, Fabrízzio Alphonsus Alves de Melo Nunes; Pedrini, Hélio; Salvini, Rogerio Lopes; Costa, Ronaldo Martins da; Cabacinha, Christian DiasFor higher productivity and economic yield in sugarcane field, several imaging techniques using sugarcane field images have been developed. However, the identification and measurement of gaps in sugarcane field crop rows are still commonly performed manually on site to decide to replant the gaps or the entire area. Manual measurement has a high cost of time and manpower. Based on these factors, this study aimed to create a new technique that automatically identifies and evaluates the gaps along the crop rows in aerial images of sugarcane fields obtained by a small remotely piloted aircraft. The images captured using the remotely piloted aircraft were used to generate the orthomosaics of the crop field area and classified with the algorithm K-Nearest Neighbors to segment the crop rows. The orientation of the planting rows in the image was found using the filter gradient Red Green Blue. Then, the crop rows were mapped using the curve adjustment method and overlap the classified image to detect and measure the gaps along the segment of the planting line. The technique developed obtained a maximum error of approximately 3% when compared to the manual method to evaluate the length of the gaps in the crop rows in an orthomosaic with an area of 8.05 hectares using the method proposed by Stolf, adapted for digital images. The proposed approach was able to properly identify the spatial position of automatically generated line segments over manually created line segments. The proposed method was also able to achieve statistically similar results when confronted with the technique performed manually in the image for the mapping of rows and identification of gaps for sugarcane fields with growth 40 and 80 days after planting. The automatic technique developed had a significant result in the evaluation of the gaps in the crop rows in the aerial images of sugarcane fields, thus, its use allows automated inspections with high accuracy measurements, and besides being able to assist producers in making decisions in the management of their sugarcane fields.