Evolutionary algorithms (EAs) and swarm algorithms (SAs) have shown their usefulness in
solving combinatorial and NP-hard optimization problems in various research fields …

Image segmentation is a very important and pre-processing step in image analysis. The
conventional multilevel thresholding methods are efficient for bi-level thresholding because …

In the paper, a three-level thresholding method for image segmentation is presented, based
on probability partition, fuzzy partition and entropy theory. A new fuzzy entropy has been …

The digital image segmentation is an open problem that is growing day by day and is
attracting the attention of researchers from last few years. Image resolution and their speed …

In this paper, we investigate the performance of the fuzzy entropy approach when it is
applied to the segmentation of infrared objects. Through a number of examples, the …

In this paper, a modified fuzzy entropy (MFE) function is proposed to perform the multilevel
thresholding of color images at different segmentation levels using Lévy flight guided firefly …

Edge detection is an important research area that finds widespread applications in various
fields, like image segmentation, shape extraction, pattern recognition, medical image …

The fuzzy clustering algorithm fuzzy c-means (FCM) is often used for image segmentation.
When noisy image segmentation is required, FCM should be modified such that it can be …

In contrast to classical image analysis methods that employ" crisp" mathematics, fuzzy set
techniques provide an elegant foundation and a set of rich methodologies for diverse image …

Image segmentation is a very significant process in image analysis. Much effort based on
thresholding has been made on this field as it is simple and intuitive, commonly used …