Many image segmentation techniques are available in the literature. Some of these
techniques use only the gray level histogram, some use spatial details while others use …

The goal of this article is to review the state-of-the-art tracking methods, classify them into
different categories, and identify new trends. Object tracking, in general, is a challenging …

Zero-shot semantic segmentation (ZS3) aims to segment the novel categories that have not
been seen in the training. Existing works formulate ZS3 as a pixel-level zero-shot …

Abstract We propose Clustering Mask Transformer (CMT-DeepLab), a transformer-based
framework for panoptic segmentation designed around clustering. It rethinks the existing …

Saliency detection aims to highlight the most relevant objects in an image. Methods using
conventional models struggle whenever salient objects are pictured on top of a cluttered …

Obtaining quantitative measures from biomedical images often requires segmentation, ie,
finding and outlining the structures of interest. Multi-modality imaging datasets, in which …

We report here on recent developments and advances in pore-scale X-ray tomographic
imaging of subsurface porous media. Our particular focus is on immiscible multi-phase fluid …

It is often a difficult task to accurately segment images with intensity inhomogeneity, because
most of representative algorithms are region-based that depend on intensity homogeneity of …

Level set methods have been widely used in image processing and computer vision. In
conventional level set formulations, the level set function typically develops irregularities …

Intensity inhomogeneity often occurs in real-world images, which presents a considerable
challenge in image segmentation. The most widely used image segmentation algorithms are …