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When confronted with objects of unknown types in an image, humans can effortlessly and precisely tell their visual boundaries. This recognition mechanism underlying generalization capability seem to contrast state-of-the-art image segmentation networks that rely on large-scale category-aware annotated training samples. In this paper, we make attempt towards building models explicitly account fo...

Microarray technology is a new and powerful tool for concurrent monitoring of large number of genes expressions. Each microarray experiment produces hundreds of images. Each digital image requires a large storage space. Hence, real-time processing of these images and transmission of them necessitates efficient and custom-made lossless compression schemes. In this paper, we offer a new archi...

In this paper, we present a novel foreground extraction method that automatically identifies image regions corresponding to a common space region seen from multiple cameras. We assume that background regions present some color coherence in each image and we exploit the spatial consistency constraint that several image projections of the same space region must satisfy. Integrating both color and...

I present an overview of the Galactic binaries that form the foreground for the ESA/NASA Laser Interferometer Space Antenna (LISA). The currently known population is discussed, as well as current and near-future large-scale surveys that will find new systems. The astrophysics that can be done when the LISA data becomes available is presented, with particular attention to verification binaries, ...

Automatic content based schemes, as opposed to those with human endeavor, have become important as users attempt to organize massive data presented in the form of multimedia data such as images, and home or movie videos. One important goal, be it in shot understanding, or scene detection, or compression, is the ability to find foreground pixels. This higher level task is best realized using a g...

For many pedestrian detectors, background vs. foreground errors heavily influence the detection quality. Our main contribution is to design semantic regions of interest that extract the foreground target roughly to reduce the background vs. foreground errors of detectors. First, we generate a pedestrian heat map from the input image with a full convolutional neural network trained on the Caltec...

We propose a method for human pose estimation which extends common unary and pairwise terms of graphical models with a global foreground term. Given knowledge of per pixel foreground, a pose should not only be plausible according to the graphical model but also explain the foreground well. However, while inference on a standard tree-structured graphical model for pose estimation can be computed...

This paper presents a new approach to spatial upsampling of digital video based on super-resolution mosaics. First, we robustly generate a background mosaic of higher resolution than the original video. In order to achieve that goal, we apply hierarchical global image registration estimating an optimal parabolic parameter set for each view of a scene shot. The final mosaic is generated using st...

We propose a method for obtaining clear underwater images by tracking the motion of suspended matter from video images captured in water and by separating the images into foreground and background. We assume that input images are the superposition of a foreground and a background, and constructed a transition model and the observation model. An input image is divided into patches and tracking o...