Ciência da Computação
URI permanente desta comunidadehttps://repositorio.fei.edu.br/handle/FEI/342
Navegar
6 resultados
Resultados da Pesquisa
Artigo de evento 1 Citação(ões) na Scopus Surface reconstruction for generating digital models of prosthesis(2011-03-05) DE AQUINO, L. C. M.; LEITE, D. A. T. Q.; GIRALDI, G. A.; CARDOSO, J. S.; Paulo Rodrigues; NEVES, L. A. P.The restoration and recovery of a defective skull can be performed through operative techniques to implant a customized prosthesis. Recently, image processing and surface reconstruction methods have been used for digital prosthesis design. In this paper we present a framework for prosthesis modeling. Firstly, we take the computed tomography (CT) of the skull and perform bone segmentation by thresholding. The obtained binary volume is processed by morphological operators, frame-by-frame, to get the inner and outer boundaries of the bone. These curves are used to initialize a 2D deformable model that generates the prosthesis boundary in each CT frame. In this way, we can fill the prosthesis volume which is the input for a marching cubes technique that computes the digital model of the target geometry. In the experimental results we demonstrate the potential of our technique and compare it with a related one.Artigo de evento 1 Citação(ões) na Scopus A study of a firefly meta-heuristics for multithreshold image segmentation(2014-10-14) ERDMANN, H.; LOPES, L. A.; Guilherme Lopes; RIBEIRO, M. P.; Paulo Rodrigues© 2014 Taylor & Francis Group, London.Thresholding-based image segmentation algorithms are usually developed for a specific set of images because the objective of these algorithms is strongly related to their applications. The binarization of the image is generally preferred over multi-segmentation, mainly because it’s simple and easy to implement. However, in this paper we demonstrate that a scene separation with three threshold levels can be more effective and closer to a manually performed segmentation. Also, we show that similar results can be achieved through a firefly-based meta-heuristic. Finally, we suggest a similarity measure that can be used for the comparison between the distances of the automatic and manual segmentation.- Computer Vision Based on a Modular Neural Network for Automatic Assessment of Physical Therapy Rehabilitation Activities(2022-01-05) FRANCISCO, J. A.; Paulo RodriguesAuthorPhysical rehabilitation techniques during the treatment of clinical pathology are one of the most challenging areas for the medical structure, patients, and families. In large and continental countries, remote monitoring of this treatment is essential. However, equipment and medical follow-up during exercises still have high costs. With the improvement of computer vision and machine learning techniques, some computational, less expensive alternatives have been proposed in the literature. However, monitoring patients during physical rehabilitation exercises with the help of artificial intelligence by a health professional, especially from the capture of visual signals, is still a challenge and poorly explored in the scientific-technological literature. This work aims to propose a new methodology based on computer vision and machine learning for remote tracking of the body joints of patients during physiotherapy rehabilitation exercises. As a new contribution, this work presents a modular neural network architecture composed of two modules: one for detecting physical exercises and another for measuring how much is correct. Another contribution is a strategy for expanding databases, considering that generic databases for this type of exercise are rare on the internet. The results showed that both modules obtained more than 90% of accuracy in recognition and their respective validation.
- CARES 3.0: A two stage system combining feature-based recognition and edge-based segmentation for CIMT measurement on a multi-institutional ultrasound database of 300 images(2011-08-30) MOLINARI, F.; MELBURGER, K. M.; ACHARYA, U. R.; ZENG, G.; Paulo Rodrigues; SABA, L.; NICOLAIDES, A.; SURI, J. S.The intima-media thickness of the carotid artery (CIMT) is a validated marker of atherosclerosis. Accurate CIMT measurement can be performed by specifically designed computer algorithms. We improved a previous CIMT measurement technique by introducing a smart heuristic search for the lumen-intima (LI) and media-adventitia (MA) interfaces of the carotid distal wall. We called this new release as CARES 3.0 (a class of AtheroEdge™ system, a patented technology from Global Biomedical Technologies, Inc., CA, USA). CARES 3.0 is completely automated and adopts an integrated approach for carotid location in the image frame, followed by segmentation based on edge snapper and heuristic search. CARES 3.0 was benchmarked against three other techniques on a 300 image multi-institutional database. One of the techniques was user-driven. The CARES 3.0 CIMT measurement bias was -0.021±0.182 mm, which was better than that of the semi automated method (-0.036±0.183 mm). CARES 3.0 outperformed the other two fully automated methods. The Figure-of-Merit of CARES 3.0 was 97.4%, better than that of the semi-automated technique (95.4%). © 2011 IEEE.
- Computing the q-index for tsallis nonextensive image segmentation(2009-10-11) Paulo Rodrigues; GIRALDI, G. A.The concept of entropy based on Shannon Theory of Information has been applied in the field of image processing and analysis since the work of T. Pun [1]. This concept is based on the traditional Boltzaman-Gibbs entropy, proposed under the classical thermodynamic. On the other hand, it is well known that this old formalism fails to explain some physical system if they have complex behavior such as long rang interactions and long time memories. Recently, studies in mechanical statistics have proposed a new kind of entropy, called Tsallis entropy (or non-extensive entropy), which has been considered with promising results on several applications in order to explain such phenomena. The main feature of Tsallis entropy is the q-index parameter, which is close related to the degree of system nonextensivity. In 2004 was proposed [2] the first algorithm for image segmentation based on Tsallis entropy. However, the computation of the q-index was already an open problem. On the other hand, in the field of image segmentation it is not an easy task to compare the quality of segmentation results. This is mainly due to the lack of an image ground truth based on human reasoning. In this paper, we propose the first methodology in the field of image segmentation for q-index computation and compare it with other similar approaches using a human based segmentation ground truth. The results suggest that our approach is a forward step for image segmentation algorithms based on Information Theory. © 2009 IEEE.
- A Bio-Inspired Strategy for 3D Surface Reconstruction of Unstructured Scenes Applied to Medical Images(2019-09-05) BOUZON, M.; ALBERTINI, G.; VIANA, G.; MEDEIROS, G.; Paulo Rodrigues© 2019 IEEE.The use of 3D reconstruction, along with immersive technologies, is a technique used in several areas of research and development. Currently, the most common strategy for performing this type of reconstruction is using a stereoscopic camera model. The problem worsens when the challenge involves unstructured scenes, which are scenes that have an ill-defined cognitive architecture. The present work proposes a methodology for 3D reconstruction of unstructured surfaces using monocular cameras. Thus, modern AI techniques, Computer Vision and Computer Graphics techniques have been applied to solve this problem. The experiments performed in this work can be concluded that the proposed method can reconstruct structured scenes with a hit rate between 63% and 68%, depending on the number of thresholds used in the segmentation, thus being superior to the classical method, where the extraction of points is done over the original image without any pre-processing.