Ciência da Computação
URI permanente desta comunidadehttps://repositorio.fei.edu.br/handle/FEI/342
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9 resultados
Resultados da Pesquisa
Artigo A study of a multi-thresholding segmentation algorithm based on bio-inspired metaheuristic and non-extensive tsallis statistics(2019) RODRIGUES, Paulo; BOUZON, M. F.; HORVATH, M.; VARELA, V. P.; LOPES, GuilhermeAn image segmentation process is one of the most important steps in an image recognition or analysis application pipeline. It is a step that splits each image into disjointed regions of interest. It is also a task that is usually performed by biological processes, such as human visual system. Due to the low processing and ease of implementation, one of the most used techniques is the thresholding method, which consists in finding the best cutting thresholds of a probability distribution histogram. However, the higher the number of thresholds, the greater the computational complexity. And there is no consensus on the number of thresholds and the partitioning position as well. This paper presents a study of the number of thresholds for segmenting an image into their regions of interest. For this purpose, the proposed method uses a bio-inspired algorithm based on meta-heuristics, called firefly with a non-extensive Tsallis statistics kernel. Also, the images are pre-filtered with a low-pass filter based on a q-gaussian function. Using a manually segmented database, the results show that there is an inverse correlation between the Fourier spectrum of an image and the number of thresholds which most approximates the image from the used ground truth. This suggests an automatic method for calculating the required number of thresholds.Artigo Automatic analysis of ocular focus detection based on visual features(2019) NASCIMENTO, D. O.; OLIVEIRA, G. A.; LOPES, Guilherme; RODRIGUES, Paulo- Improving a firefly meta-heuristic for multilevel image segmentation using Tsallis entropy(2015) RODRIGUES, Paulo; LOPES, Guilherme; ERDMANN, H. R.; RIBEIRO, M. P.; GIRALDI, G. A.In this paper we show that the non-extensive Tsallis entropy, when used as kernel in the bio-inspired firefly algorithm for multi-thresholding in image segmentation, is more efficient than using the traditional crossentropy resented in the literature. The firefly algorithm is a swarm-based meta-heuristic, inspired by fireflies-seeking behavior following their luminescence. We show that the use of more convex kernels, as those based on non-extensive entropy, is more effective at 5 % of significance level than the cross-entropy counterpart when applied in synthetic spaces for searching thresholds in global minimum
- Dimensionality reduction, classification and reconstruction problems in statistical learning approaches(2008) GIRALDI, G. A.; RODRIGUES, Paulo; KITANI, E. C.; THOMAZ, C.
- Fourier analysis and q-Gaussian functions: analytical and numerical results(2016) RODRIGUES, Paulo; GIRALDI, G.
- Non-extensive entropy algorithm for multi-region segmentation: generalization and comparison(2013) RODRIGUES, Paulo; GIRALDI, G. A.
- Improving the non-extensive medical image segmentation based on Tsallis entropy(2011) RODRIGUES, Paulo; GIRALDI, G. A.Thresholding techniques for image segmentation is one of the most popular approaches in Computational Vision systems. Recently, M. Albuquerque has proposed a thresholding method (Albuquerque et al. in Pattern Recognit Lett 25:1059–1065, 2004) based on the Tsallis entropy, which is a generalization of the traditional Shannon entropy through the introduction of an entropic parameter q. However, the solution may be very dependent on the q value and the development of an automatic approach to compute a suitable value for q remains also an open problem. In this paper, we propose a generalization of the Tsallis theory in order to improve the non-extensive segmentation method. Specifically, we work out over a suitable property of Tsallis theory, named the pseudo-additive property, which states the formalism to compute the whole entropy from two probability distributions given an unique q value. Our idea is to use the original M. Albuquerque’s algorithm to compute an initial threshold and then update the q value using the ratio of the areas observed in the image histogram for the background and foreground. The proposed technique is less sensitive to the q value and overcomes the M. Albuquerque and k-means algorithms, as we will demonstrate for both ultrasound breast cancer images and synthetic data.
- Statistical learning approaches for discriminant features selection(2008) GIRALDI, G. A.; RODRIGUES, Paulo; KITANI, E. C.; SATO, J. R.; THOMAZ, C. E.
- Network anomaly detection using nonextensive entropy(2007) ZIVIANI, A.; GOMES, A. T. A.; MONSORES, M. L.; RODRIGUES, Paulo