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) …

Most recent semantic segmentation methods train deep convolutional neural networks with
fully annotated masks requiring pixel-accuracy for good quality training. Common weakly …

We introduce an unsupervised, geodesic distance based, salient video object segmentation
method. Unlike traditional methods, our method incorporates saliency as prior for object via …

Interactive object selection is a very important research problem and has many applications.
Previous algorithms require substantial user interactions to estimate the foreground and …

In this paper, we propose DeepCut, a method to obtain pixelwise object segmentations
given an image dataset labelled weak annotations, in our case bounding boxes. It extends …

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 …

Interactive image segmentation is characterized by multimodality. When the user clicks on a
door, do they intend to select the door or the whole house? We present an end-to-end …

Segmentation of organs or lesions from medical images plays an essential role in many
clinical applications such as diagnosis and treatment planning. Though Convolutional …

Segmentation is the process of partitioning digital images into meaningful regions. The
analysis of biological high content images often requires segmentation as a first step. We …