The investigation of uncertainty is of major importance in risk-critical applications, such as
medical image segmentation. Belief function theory, a formal framework for uncertainty …

Intensity inhomogeneities often occur in real-world images and may cause considerable
difficulties in image segmentation. In order to overcome the difficulties caused by intensity …

In this paper, we propose a natural framework that allows any region-based segmentation
energy to be re-formulated in a local way. We consider local rather than global image …

As one of the most important tasks in biomedical imaging, image segmentation provides the
foundation for quantitative reasoning and diagnostic techniques. A large variety of different …

In this paper, an efficient algorithm for texture recognition of synthetic aperture radar (SAR)
images is developed based on wavelet transform as a feature extraction tool and support …

Detecting COVID-19 from medical images is a challenging task that has excited scientists
around the world. COVID-19 started in China in 2019, and it is still spreading even now …

Image processing is a technique which is used to derive information from the images.
Segmentation is a section of image processing for the separation or segregation of …

We propose and analyze a nonparametric region-based active contour model for
segmenting cluttered scenes. The proposed model is unsupervised and assumes pixel …

Texture-based segmentation of synthetic aperture radar (SAR) image is a difficult task in
remote sensing applications because it must address the problem of speckle noise. Several …

In recent years, the acquisition of image and video information for processing, analysis,
understanding, and exploitation of the underlying content in various applications, ranging …