This paper explores the use of the Learning Automata (LA) algorithm to compute threshold
selection for image segmentation as it is a critical preprocessing step for image analysis …

A method is proposed seeking thresholds for segmentation of grayscale images. The
normalized image histogram is modeled as a Gaussian mixture, and the parameters …

In the field of image analysis, segmentation is one of the most important preprocessing
steps. One way to achieve segmentation is the use of threshold selection, where each pixel …

Threshold selection is a critical preprocessing step for image analysis, pattern recognition
and computer vision. On the other hand differential evolution (DE) is a heuristic method for …

In the field of image analysis, segmentation is one of the most important preprocessing
steps. One way to achieve segmentation is by mean of threshold selection, where each pixel …

The separation of an object from other objects or the background by selecting the optimal
threshold values remains a challenge in the field of image segmentation. Threshold …

This paper presents a novel optimal multilevel thresholding algorithm for histogram-based
image segmentation. The proposed algorithm presents an improved variant of the …

Despite the great prosperity and fast development of image segmentation technology,
threshold selection method is still the best choice in many practical applications. The state-of …

Segmentation of nontrivial images is one of the most important tasks in image processing. It
is easy for human being, but extremely difficult for computers. With the purpose of finding …

Neural network based image segmentation techniques primarily focus on the selection of
appropriate thresholding points in the image feature space. Research initiatives in this …