The maximum entropy principle is largely used in thresholding and segmentation of images. Among the several formulations of this principle, the most effectively applied is that based on Tsallis non-extensive entropy. Here, we discuss the role of its entropic index in determining the thresholds. When this index is spanning the interval (0,1), for some images, the values of thresholds can have large leaps. In this manner, we observe abrupt transitions in the appearance of corresponding bi-level or multi-level images. These gray-level image transitions are analogous to order or texture transitions observed in physical systems, transitions which are driven by the temperature or by other physical quantities
In this dissertation, we discuss multi-level image thresholding techniques based on information theo...
Automatic thresholding of the gray-level values of an image is very useful in automated analysis of ...
Renyi entropy based image thresholding in [Sahoo, P., Wilkins, C., et al., 1997. Thresholding select...
The maximum entropy principle is largely used in thresholding and segmentation of images. Among the ...
The maximum entropy principle has a relevant role in image processing, in particular for thresholdin...
The maximum entropy principle is often used for bi-level or multi-level thresholding of images. For ...
In image processing, the maximum entropy principle is often used for the elaboration of images, in p...
The Boltzmann–Gibbs and Tsallis entropies are essential concepts in statistical physics, which have ...
In image processing, the maximum entropy principle is generally recognized as having a relevant role...
The maximum entropy principle is often used for bi-level or multi-level thresholding of images. For ...
This paper deals with an entropic approach as unsupervised thresholding technique for image processi...
Entropy of order q (depending on the information contained in a sequence of gray levels of length q)...
International audienceThis paper deals with an entropic approach as unsupervised thresholding techni...
Vol. IInternational audienceThis paper deals with an entropic approach as unsupervised thresholding ...
Two methods of entropic thresholding proposed by Pun (Signal Process.,2, 1980, 223-237;Comput. Graph...
In this dissertation, we discuss multi-level image thresholding techniques based on information theo...
Automatic thresholding of the gray-level values of an image is very useful in automated analysis of ...
Renyi entropy based image thresholding in [Sahoo, P., Wilkins, C., et al., 1997. Thresholding select...
The maximum entropy principle is largely used in thresholding and segmentation of images. Among the ...
The maximum entropy principle has a relevant role in image processing, in particular for thresholdin...
The maximum entropy principle is often used for bi-level or multi-level thresholding of images. For ...
In image processing, the maximum entropy principle is often used for the elaboration of images, in p...
The Boltzmann–Gibbs and Tsallis entropies are essential concepts in statistical physics, which have ...
In image processing, the maximum entropy principle is generally recognized as having a relevant role...
The maximum entropy principle is often used for bi-level or multi-level thresholding of images. For ...
This paper deals with an entropic approach as unsupervised thresholding technique for image processi...
Entropy of order q (depending on the information contained in a sequence of gray levels of length q)...
International audienceThis paper deals with an entropic approach as unsupervised thresholding techni...
Vol. IInternational audienceThis paper deals with an entropic approach as unsupervised thresholding ...
Two methods of entropic thresholding proposed by Pun (Signal Process.,2, 1980, 223-237;Comput. Graph...
In this dissertation, we discuss multi-level image thresholding techniques based on information theo...
Automatic thresholding of the gray-level values of an image is very useful in automated analysis of ...
Renyi entropy based image thresholding in [Sahoo, P., Wilkins, C., et al., 1997. Thresholding select...