Group-wise image alignment or image congealing is an image processing technique which allows the joint alignment of a collection of images. Typically, information theoretic metrics have been employed as the objective function for the assessment of the process of alignment of the images for such methods. However, these objective functions rely on their probabilistic foundations and cannot model the underlying vagueness or uncertainty that is captured by approaches such as those based on fuzzy sets. In this paper a novel fuzzy-entropy based approach is presented for the task of image congealing. This approach allows for much flexibility in terms of employing different definitions for both similarity and fuzzy-entropy. Indeed, the existing app...
Several entropy measures are now widely used to analyze real-world time series. Among them, we can c...
In this paper, we have proposed a method for segmentation of lungs from Computed Tomography (CT)-sca...
Several entropy measures are now widely used to analyze real-world time series. Among them, we can c...
Group-wise image alignment or image congealing is an image processing technique which allows the joi...
Image Congealing (IC) is a non-parametric method for the joint alignment of a collection of images a...
Image segmentation using fuzzy entropy is an important and common segmentation method. The threshold...
In this paper, we propose a novel approach for image segmentation via fuzzification of Rènyi Entropy...
The paper introduces entropy as a measure for 1D signals. We propose an entropy measure of the relat...
Multimodal medical images are useful for observing tissue structure clearly in clinical practice. To...
The definition of Shannon's entropy in the context of information theory is critically examined and ...
Contrast enhancement is a very important issue in image processing, pattern recognition and computer...
The entropy method for image thresholding suggested by Kapur et al. has been modified and a more per...
Soft computing is likely to play aprogressively important role in many applications including image ...
AbstractReferenced image quality assessment methods require huge memory and time involvement, theref...
It is not a surprise that image processing is a growing research field. Vision in general and images...
Several entropy measures are now widely used to analyze real-world time series. Among them, we can c...
In this paper, we have proposed a method for segmentation of lungs from Computed Tomography (CT)-sca...
Several entropy measures are now widely used to analyze real-world time series. Among them, we can c...
Group-wise image alignment or image congealing is an image processing technique which allows the joi...
Image Congealing (IC) is a non-parametric method for the joint alignment of a collection of images a...
Image segmentation using fuzzy entropy is an important and common segmentation method. The threshold...
In this paper, we propose a novel approach for image segmentation via fuzzification of Rènyi Entropy...
The paper introduces entropy as a measure for 1D signals. We propose an entropy measure of the relat...
Multimodal medical images are useful for observing tissue structure clearly in clinical practice. To...
The definition of Shannon's entropy in the context of information theory is critically examined and ...
Contrast enhancement is a very important issue in image processing, pattern recognition and computer...
The entropy method for image thresholding suggested by Kapur et al. has been modified and a more per...
Soft computing is likely to play aprogressively important role in many applications including image ...
AbstractReferenced image quality assessment methods require huge memory and time involvement, theref...
It is not a surprise that image processing is a growing research field. Vision in general and images...
Several entropy measures are now widely used to analyze real-world time series. Among them, we can c...
In this paper, we have proposed a method for segmentation of lungs from Computed Tomography (CT)-sca...
Several entropy measures are now widely used to analyze real-world time series. Among them, we can c...