Open international challenges are becoming the de facto standard for assessing computer
vision and image analysis algorithms. In recent years, new methods have extended the …

Cracks are usually curve-like structures that are the focus of many computer-vision
applications (eg, road safety inspection and surface inspection of industrial facilities). The …

Segmentation of curvilinear structures is important in many applications, such as retinal
blood vessel segmentation for early detection of vessel diseases and pavement crack …

Curvilinear object segmentation is critical for many applications. However, manually
annotating curvilinear objects is very time-consuming and error-prone, yielding insufficiently …

Segmentation models predominantly optimize pixel-overlap-based loss, an objective that is
actually inadequate for many segmentation tasks. In recent years, their limitations fueled a …

Vessel segmentation is crucial in many medical image applications, such as detecting
coronary stenoses, retinal vessel diseases and brain aneurysms. However, achieving high …

Retinal vessels are essential biomarkers for the early diagnosis of ophthalmic diseases.
Accurate segmentation of retinal vessels is necessary for further quantitative analysis of …

Weakly-supervised learning (WSL) has been proposed to alleviate the conflict between data
annotation cost and model performance through employing sparsely-grained (ie, point-, box …

Pixel-level crack extraction (PCE) is challenging due to topology complexity, irregular edges,
low contrast ratio, and complex background. Recently, Transformer architectures have …

Road extraction from high-resolution remote sensing images has long been a focal and
challenging research topic in the field of computer vision. Accurate extraction of road …