Image thresholding using Tsallis entropy

By Marcelo Portes de Albuquerque, Israel A. Esquef, A. R. Gesualdi, Márcio Portes de Albuquerque. Pattern Recognition Letters, 2004

535 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Economics and Econometrics, Statistical and Nonlinear Physics

View Paper: https://www.semanticscholar.org/paper/330405a99574c0cbd7e09d24de1353315788689b

How researchers see this paper

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In Computer Vision and Pattern Recognition

1. D. Wildenschild
2. Steffen Schlüter
3. Ishita Das
4. Adrian P. Sheppard
5. Ujjwal Maulik
6. Nibaran Das
7. Kendra I. Brown
8. Swarnendu Ghosh

In Statistical and Nonlinear Physics

1. Ujjwal Maulik
2. M. Reibel

Citations in the corpus

• Image processing of multiphase images obtained via X‐ray microtomography: A review (2014, methodology): “Shannon entropy can be replaced by Tsallis entropy for maximum entropy thresholding, allowing for an additional degree of freedom that can be used to tune the results [de Albuquerque et al., 2004].”
• Understanding Deep Learning Techniques for Image Segmentation (2019, methodology): “h as histogram thresholding [195], hybridization [193, 87] feature space clustering [40], regionbased approaches [59], edge detection approaches [184], fuzzy approaches [39], entropy-based approaches [47], neural networks (Hopeld neural network [35], self-organizing maps [27]), physics-based approac”
• Optical music recognition: state-of-the-art and open issues (2012, unknown): “[29], .”