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In this paper, we propose a framework for isolating text regions from natural scene images. The main algorithm has two functions: it generates text region candidates, and it verifies of the label of the candidates (text or non-text). The text region candidates are generated through a modified Kmeans clustering algorithm, which references texture features, edge information and color information. The candidate labels are then verified in a global sense by the Markov Random Field model where collinearity weight is added as long as most texts are aligned. The proposed method achieves reasonable accuracy for text extraction from moderately difficult examples from the ICDAR 2003 database.

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Improving the quality of degraded images is a key problem in image processing, but the breadth of the problem leads to domain-specific approaches for tasks such as super-resolution and compression artifact removal. Recent approaches have shown that a general approach is possible by learning application-specific models from examples; however, learning models sophisticated enough to generate high-quality images is computationally expensive, and so specific per-application or per-dataset models are impractical. To solve this problem, we present an efficient semi-local approximation scheme to large-scale Gaussian processes. This allows efficient learning of task-specific image enhancements from example images without reducing quality. As such, our algorithm can be easily customized to specific applications and datasets, and we show the efficiency and effectiveness of our approach across five domains: single-image super-resolution for scene, human face, and text images, and artifact removal in JPEG- and JPEG 2000-encoded images.

Imidazolium-based ionic liquids (ILs) bearing an alkylphosphite anion, were highly efficient for the selective removal of acetylenes in olefins. Comparison of solubility data at 313 K and at atmospheric pressure shows that the solubilities of acetylene and propyne in 1,3-dimethylimidazolium methylphosphite ([DMIM][MeHPO(3)]) are about 45 and 20 times higher than those of ethylene and propylene, respectively. Computational and (1)H NMR results clearly demonstrate that there are substantial interactions between the acidic hydrogen atom or atoms of acetylenes and the phosphite anion.