Automatic liver segmentation from abdominal computed tomography (CT) images based on gray levels or shape alone is difficult because of the overlap in gray-level ranges and the variation in position and shape of the soft tissues. To address these issues, we propose an automatic liver segmentation method that utilizes low-level features based on texture information; this texture information is e...

Background: Accurate brain tissue segmentation from magnetic resonance (MR) images is an important step in analysis of cerebral images. There are software packages which are used for brain segmentation. These packages usually contain a set of skull stripping, intensity non-uniformity (bias) correction and segmentation routines. Thus, assessment of the quality of the segmented gray matter (GM), ...

Discovering and segmenting objects in videos is a challenging task due to large variations of objects in appearances, deformed shapes and cluttered backgrounds. In this paper, we propose to segment objects and understand their visual semantics from a collection of videos that link to each other, which we refer to as semantic co-segmentation. Without any prior knowledge on videos, we first extra...

Co-segmentation is the automatic extraction of the common semantic regions given a set of images. Different from previous approaches mainly based on object visuals, in this paper, we propose a human centred object co-segmentation approach, which uses the human as another strong evidence. In order to discover the rich internal structure of the objects reflecting their human-object interactions a...

We present the problem of automatic co-niching in which potential suppliers of some product or service need to determine which offers to make to the marketplace at the same time as potential buyers need to determine which offers (if any) to purchase. Because both groups typically face incomplete or uncertain information needed for these decisions, participants in repeated market interactions en...

In computer vision, co-segmentation is defined as the task of jointly segmenting the common objects in a given set of images. Most proposed cosegmentation algorithms have the assumptions that the common objects are singletons or with the similar size. In addition, they might assume that the background features are simple or discriminative. This paper presents a cooperative co-segmentation witho...

The aim of this paper is to leverage foreground segmentation to improve classification performance on weakly annotated datasets – those with no additional annotation other than class labels. We introduce TriCoS, a new co-segmentation algorithm that looks at all training images jointly and automatically segments out the most class-discriminative foregrounds for each image. Ultimately, those fore...

To obtain high-quality segmentation results the integration of semantic information is indispensable. In contrast to existing segmentation methods which use a spatial regularizer, i.e. a local interaction between image points, the co-occurrence prior [15] imposes penalties on the co-existence of different labels in a segmentation. We propose a continuous domain formulation of this prior, using ...