A great number of improved fuzzy c-means (FCM) clustering algorithms have been widely
used for grayscale and color image segmentation. However, most of them are time …

Most existing bottom-up methods measure the foreground saliency of a pixel or region
based on its contrast within a local context or the entire image, whereas a few methods focus …

Open-world instance segmentation is the task of grou** pixels into object instances
without any pre-determined taxonomy. This is challenging, as state-of-the-art methods rely …

Image segmentation is a fundamental problem in computer vision. Despite many years of
research, general purpose image segmentation is still a very challenging task because …

Grou** cues can affect the performance of segmentation greatly. In this paper, we show
that superpixels (image segments) can provide powerful grou** cues to guide …

Object proposals are a series of candidate segments containing the objects of interest,
which are taken as preprocessing and widely applied in various vision tasks. However, most …

Surface defect detection of strip steel is still a challenging task for its complex variances, eg,
intra-class defects exist large differences in appearance while inter-class defects contain …

In the machine learning and pattern recognition fraternity, uncertain data clustering is an
essential job because uncertainty in data makes the clustering process more difficult …

Morphological reconstruction (MR) is often employed by seeded image segmentation
algorithms such as watershed transform and power watershed, as it is able to filter out seeds …

Most existing bottom-up algorithms measure the foreground saliency of a pixel or region
based on its contrast within a local context or the entire image, whereas a few methods focus …