This paper is concerned with the problem of image completion where the goal is to fill large missing parts (holes) in an image, video or a scene in a visually-plausible and a computationally-efficient manner. Recently, the literature on hole filling was dominated by exemplar-based (patch-based) filling techniques with a two-stage unified pipeline that starts by building a bag of significant patches (BoSP), and then uses that bag to fill the hole. In this paper, we propose a new framework which addresses the inherent limitations of the state-of-the-art techniques. Our method capitalizes on a newly-developed technique for image skimming, followed by a novel procedure to propagate the constructed skim to within the hole. Experimental results show that our method compares favourably with the state-of-the-art.
In this paper, we are concerned with unsupervised natural image matting. Due to the under-constrained nature of the problem, image matting algorithms are usually provided with user interactions, such as scribbles or trimaps. This is a very tedious task and may even become impractical for some applications. For unsupervised matte calculation, we can either adopt a technique that supports an unsupervised mode for alpha map calculation, or we may automate the process of acquiring user interactions provided for a matting algorithm. Our proposed technique contributes to both approaches and is based on spectral matting. The latter is the only technique in the literature that supports automatic matting but it suffers from critical limitations among which is the unreliable unsupervised operation. Stressing on that drawback, spectral matting may produce erroneous mattes in the absence of guiding scribbles or trimaps. Using the Gestalt laws of grouping, we propose a method that automatically produces more truthful mattes than spectral matting. In addition, it can be used to generate trimaps, eliminating the required user interactions and making it possible to harness the powers of matting techniques that are better than spectral matting but don't support unsupervised operation. The main contribution of this research is the introduction of the Gestalt laws of grouping to the matting problem.
This paper addresses the problem of natural image matting in which the goal is to softly-segment a foreground from a background. Given an input image and some known foreground (FG) and background (BG) pixels, an alpha value indicating a partial foreground coverage is calculated for every other pixel in the image. The proposed algorithm is affiliated to the sampling-based matting techniques where the alpha of every unknown pixel is calculated using some FG / BG pairs that are sampled according to certain criteria. Current sampling based matting techniques suffer from critical disadvantages, leaving the problem open for further development. By adopting a novel FG / BG pair-selection strategy, we propose a technique that overcomes critical pitfalls in the state-of-the-art methods with a performance that is comparable (and superior in certain cases) to them. Our results were evaluated according to the matting online benchmark.
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