Ciência da Computação
URI permanente desta comunidadehttps://repositorio.fei.edu.br/handle/FEI/342
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3 resultados
Resultados da Pesquisa
- A q-extension of sigmoid functions and the application for enhancement of ultrasound images(2019) Rodrigues P.S.; Wachs-Lopes G.; Santos R.M.; Coltri E.; Giraldi G.A.© 2019 by the authors.This paper proposes the q-sigmoid functions, which are variations of the sigmoid expressions and an analysis of their application to the process of enhancing regions of interest in digital images. These new functions are based on the non-extensive Tsallis statistics, arising in the field of statistical mechanics through the use of q-exponential functions. The potential of q-sigmoids for image processing is demonstrated in tasks of region enhancement in ultrasound images which are highly affected by speckle noise. Before demonstrating the results in real images, we study the asymptotic behavior of these functions and the effect of the obtained expressions when processing synthetic images. In both experiments, the q-sigmoids overcame the original sigmoid functions, as well as two other well-known methods for the enhancement of regions of interest: slicing and histogram equalization. These results show that q-sigmoids can be used as a preprocessing step in pipelines including segmentation as demonstrated for the Otsu algorithm and deep learning approaches for further feature extractions and analyses.
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.- A strategy based on non-extensive statistics to improve frame-matching algorithms under large viewpoint changes(2019) LOPES, Guilherme; HORVATH, M.; GIRALDI, G. A.; LOPES, Guilherme