Ciência da Computação
URI permanente desta comunidadehttps://repositorio.fei.edu.br/handle/FEI/342
Navegar
5 resultados
Resultados da Pesquisa
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.Artigo de evento 0 Citação(ões) na Scopus Automatic source code correction system focused on teacher's usability(2019-03-12) BELTRAME, F. S.; MAIA, R. F.; Guilherme LopesCopyright © 2019 by the International Institute ofInformatics and Systemics.In the Computing area many workout correction systems are used for automatic correction of source codes. However, one of the main impediments to the use of these tools is the inefficiency of the interface for the elaboration and use of the teacher in their discipline. In this project, we propose an automatic correction system for source codes so that the teacher can elaborate his own corrector in a practical manner. This corrector is used as a comparison guideline for submitted exercises.- Parallel approach to the firefly algorithm(2018-10-05) LINHARES, G.; COSTA, G.; JACOBSON, L.; RODRIGUES, L.; BALLABENUTE, V.; Guilherme Lopes; RODRIGUES, P.© 2018 IEEE.Distributed computing is a computation approach in which many calculations are made at the same time in a distributed memory model, exploring the fact that big problems can sometimes be divided into little ones that can be solved at the same time. This paper uses the distributed computing concept to optimize the sequential version of the Firefly Algorithm (FA). Results show that the proposed distributed version is more efficient than the regular existing algorithm.
- Image Stitching Using Non-Extensive Statistics(2019-09-05) CARDOSO, E.; RISCH, H. A.; LAHERAS, L. P.; LUIZ, V.; RODRIGUES, P. S.; Guilherme Lopes© 2019 IEEE.Nowadays there are different ways to make image stitching with help of Fiducial Point Descriptors (FPD), whose find the matches between images for an application, such as SIFT and calculates the homography with RANSAC. However, by finding the right match when we have images on differents points of view could be difficult. This paper introduces the application of q-SFT, a newest variation of SFT in a stitch algorithm, that can recognize large viewpoint changes called as LVC.
- CAD system for breast US images with speckle noise reduction and bio-inspired segmentation(2019-10-05) RODRIGUES, P. S. S.; Guilherme Lopes; GIRALDI, G. A.; BARCELOS, C. A. Z.; VIEIRA, L.; GULIATO, D.; KUMAR SINGH, B.© 2019 IEEE.Ultrasound (US) images are highly susceptible to speckle-like noise which makes imperative to use specific techniques for image smoothing. However, this process can lead to undesirable side effects such as the degradation of the real contour of the region of interest (ROI). In such context, this paper presents a new methodology for computer aided diagnosis (CAD) systems whose heart is the combination of a method for speckle noise reduction, with histogram equalization and a technique for image segmentation that uses the bio-inspired firefly algorithm and Bayesian model. The segmentation approach and the equalization are applied in two distinct stages: globally and locally. The global application produces an initial coarse estimate of the ROI, and the local application defines this region more precisely. In the classification step we carried out experiments which show that the combination of features computed both within and below the lesion strongly influences the final accuracy. We show that the gray-scale distribution and statistical moments within the lesion together with gray-scale distribution and contrast of the region below the lesion is the combination that produces the better classification results. Experiments in a database of 250 US images of breast anomalies (100 benign and 150 malignant) show that the proposed methodology reaches performance of 95%.