In image processing, the maximum entropy principle is often used for the elaboration of images, in particular to distinguish in them the objects from the background, through a process of image segmentation. Different formulations of the image entropy are available to this purpose, but the most prominent in recent publications, in particular in those concerning the medical image processing, is that of the Tsallis non-extensive entropy. Here, we discuss and show some examples of segmentation with this specific entrop
The Boltzmann–Gibbs and Tsallis entropies are essential concepts in statistical physics, which have ...
The maximum entropy principle is often used for bi-level or multi-level thresholding of images. For ...
The paper introduces entropy as a measure for 1D signals. We propose an entropy measure of the relat...
In image processing, the maximum entropy principle is often used for the elaboration of images, in p...
In image processing, the maximum entropy principle is generally recognized as having a relevant role...
The maximum entropy principle has a relevant role in image processing, in particular for thresholdin...
The definition of Shannon's entropy in the context of information theory is critically examined and ...
Keywords:image; segmentation; analysis; algorithm; maximum entropy; Abstract. This paper introduces ...
The maximum entropy principle is largely used in thresholding and segmentation of images. Among the ...
Image segmentation plays an important role in medical imaging applications. Therefore, accurate meth...
Image is made of up pixels that contain some information. The dossier load of an image is measured b...
This paper presents a thorough study of different types of entropies. Application and comparison of ...
Abstract: The definition of Shannon's entropy in the context of infonnation theory is criticall...
Image segmentation is a significant step in image analysis and computer vision. Many entropy based a...
Image analysis is a fundamental task for extracting information from images acquired across a range ...
The Boltzmann–Gibbs and Tsallis entropies are essential concepts in statistical physics, which have ...
The maximum entropy principle is often used for bi-level or multi-level thresholding of images. For ...
The paper introduces entropy as a measure for 1D signals. We propose an entropy measure of the relat...
In image processing, the maximum entropy principle is often used for the elaboration of images, in p...
In image processing, the maximum entropy principle is generally recognized as having a relevant role...
The maximum entropy principle has a relevant role in image processing, in particular for thresholdin...
The definition of Shannon's entropy in the context of information theory is critically examined and ...
Keywords:image; segmentation; analysis; algorithm; maximum entropy; Abstract. This paper introduces ...
The maximum entropy principle is largely used in thresholding and segmentation of images. Among the ...
Image segmentation plays an important role in medical imaging applications. Therefore, accurate meth...
Image is made of up pixels that contain some information. The dossier load of an image is measured b...
This paper presents a thorough study of different types of entropies. Application and comparison of ...
Abstract: The definition of Shannon's entropy in the context of infonnation theory is criticall...
Image segmentation is a significant step in image analysis and computer vision. Many entropy based a...
Image analysis is a fundamental task for extracting information from images acquired across a range ...
The Boltzmann–Gibbs and Tsallis entropies are essential concepts in statistical physics, which have ...
The maximum entropy principle is often used for bi-level or multi-level thresholding of images. For ...
The paper introduces entropy as a measure for 1D signals. We propose an entropy measure of the relat...