Image segmentation refers to the process to divide an image into meaningful non-
overlap** regions according to human perception, which has become a classic topic since …

Image segmentation is one of the most basic tasks in computer vision and remains an initial
step of many applications. In this paper, we focus on interactive image segmentation (IIS) …

Segmentation algorithms are prone to make topological errors on fine-scale struc-tures, eg,
broken connections. We propose a novel method that learns to segment with correct …

Accurate segmentation of tubular, network-like structures, such as vessels, neurons, or
roads, is relevant to many fields of research. For such structures, the topology is their most …

Salient object detection has been attracting a lot of interest, and recently various heuristic
computational models have been designed. In this paper, we regard saliency map …

Manually annotating object bounding boxes is central to building computer vision datasets,
and it is very time consuming (annotating ILSVRC [53] took 35s for one high-quality box …

Generic object level saliency detection is important for many vision tasks. Previous
approaches are mostly built on the prior that “appearance contrast between objects and …

Humans perceive the three-dimensional structure of the world with apparent ease. However,
despite all of the recent advances in computer vision research, the dream of having a …

We present a novel framework to generate and rank plausible hypotheses for the spatial
extent of objects in images using bottom-up computational processes and mid-level …

We describe a method to automatically detect contours, ie lines along which the surface
orientation sharply changes, in large-scale outdoor point clouds. Contours are important …