Smart Scribbles for Image Matting

Smart Scribbles for Sketch Segmentation

We present Smart Scribbles—a new scribble-based interface for user-guided segmentation of digital sketchy drawings. In contrast to previous approaches based on simple selection strategies, Smart Scribbles exploits richer geometric and temporal information, resulting in a more intuitive segmentation interface. We introduce a novel energy minimization formulation in which both geometric and tempo...

Interactive Image Matting using Optimization

In this paper, we formulate interactive image matting as a constrained optimization problem. We first make some simple and reasonable assumptions about the alpha matte and assume that, geometrically, the closer two pixels are, the more likely they will have similar alpha values, conversely, the farther apart two pixels are, the more likely they will have different alpha values; photometrically,...

Grayscale Image Matting and Colorization

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...

Confidence-driven image co-matting

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...

Towards Efficient and Accurate Image Matting