Single image matting, the task of estimating accurate foreground opacity from a given image, is a severely ill-posed and challenging problem. Inspired by recent advances in image co-segmentation, in this paper, we present a novel framework for a new task called co-matting, which aims to simultaneously extract alpha mattes in multiple images that contain slightly-deformed instances of the same f...

Natural image matting, which separates foreground from background, is a very important intermediate step in recent computer vision algorithms. However, it is severely underconstrained and difficult to solve. State-of-the-art approaches include matting by graph Laplacian, which significantly improves the underconstrained nature by reducing the solution space. However, matting by graph Laplacian ...

This paper presents a novel approach to grayscale image matting and colorization. The first part of this approach is an efficient grayscale image matting algorithm in Bayesian framework. The foreground and background color distributions, and the alpha’s distribution are modelled with spatially varying sets of Gaussians. The major novelties of this matting algorithm are the introduction of alpha...

This literature review attempts to provide a brief overview of some of the most common segmentation techniques, and a comparison between them. It discusses the “Grab-Cut” technique, and reviews some some of the common matting techniques. The graph cut approaches to segmentation can be extended to 3-D data and can be used for segmenting 3-D volumes. Other segmentation techniques use either conto...

Previous video matting approaches mostly adopt the “binary segmentation + matting” strategy, i.e., first segment each frame into foreground and background regions, then extract the fine details of the foreground boundary using matting techniques. This framework has several limitations due to the fact that binary segmentation is employed. In this paper, we propose a new supervised video matting ...

Model updating is a critical problem in tracking. Inaccurate extraction of the foreground and background information in model adaptation would cause the model to drift and degrade the tracking performance. The most direct but yet difficult solution to the drift problem is to obtain accurate boundaries of the target. We approach such a solution by proposing a novel closed-loop model adaptation f...

Video matting is the process of taking a sequence of frames, isolating the foreground, and replacing the background with something different in each frame. This is an under-constrained problem when the background is unknown. Matting techniques exist to approximate these values using manual input cues. We look at existing singleframe matting techniques and present a method that improves upon the...

Image matting aims to extract foreground objects from a given image in a fuzzy mode. One of the major state-of-the-art methods in this field is spectral matting. It automatically computes fuzzy matting components by using the smallest eigenvectors of a defined Laplacian matrix that is generated from affinities computation between adjacent pixels in an image. Results obtained by such approach ar...

Image matting is an important vision problem. The main stream methods for it combine sampling-based methods and propagation-based methods. In this paper, we deal with the combination with a normalized weighting parameter, which could well control the relative relationship between information from sampling and from propagation. A reasonable value range for this parameter is given based on statis...

The objective of this paper is to obtain pixel-accurate reconstructions of white shark fins given automatically generated coarse pre-segmentations. Reconstruction performance is compared for affinity matting, colour matting and GrabCut against expert annotated ground truth for a test-set of 120 fin images taken in the wild. For the present domain, we find affinity matting able to most accuratel...