CEIG2024
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Browsing CEIG2024 by Subject "Computer graphics"
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Item An Efficient Point Selection Process over a Meshlet-structured Point Cloud(The Eurographics Association, 2024) Ortega, Lidia M.; Fernández, Juan Carlos; Collado, José Antonio; Feito, J. Francisco R.; Marco, Julio; Patow, GustavoVisualizing and interacting increasingly large and dense point clouds imposes the need for new methods with real-time results, where most common solutions imply a disadvantage greater than their benefit. Among the recent software and hardware advances in computer graphics and visualization, it is possible to take the concept of meshlet as a clustering of nearby points in space; this nature can bring a considerable improvement in the interaction process over the classical brute-force based algorithm, similar to common three-dimensional spatial structures. This work implements the point selection process over a meshlet-structured point cloud, assessing its performance against alternative methods and validating its correctness by visualizing the selection result on a graphical interface. By exploiting the meshlet instead of building additional spatial structures, the method's execution time can be optimized, as well as the use of the system's main memory.Item Enhancing Medical Diagnosis and Treatment Planning through Automated Acquisition and Classification of Bone Fracture Patterns(The Eurographics Association, 2024) Pérez-Cano, Francisco Daniel; Parra-Cabrera, Gema; Camacho-García, Rubén; Jiménez, Juan José; Marco, Julio; Patow, GustavoThe extraction of the main features of a fractured bone area enables subsequent virtual reproduction for bone simulations. Exploring the fracture zone for other applications remains largely unexplored in current research. Recreating and analyzing fracture patterns has direct applications in medical training programs for traumatologists, automatic bone fracture reduction algorithms, and diagnostics. Furthermore, pattern classification aids in establishing treatment guidelines that specialists can follow during the surgical process. This paper focuses on the process of obtaining an accurate representation of bone fractures, starting with computed tomography scans, and subsequently classifying these patterns using a convolutional neural network. The proposed methodology aims to streamline the extraction and classification of fractures from clinical cases, contributing to enhanced diagnosis and medical simulation applications.