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During the last several decades, numerous researchers have provided evidence that physical and biogeochemical processes at air-snow/ice-water interfaces are very complex and, in many cases, interlinked. This review focuses on...

Motion blur appearing in traffic sign images may lead to poor recognition results, and therefore it is of great significance to study how to deblur the images. In this paper, a novel method for deblurring traffic sign is proposed based on exemplars and several related approaches are also made. First, an exemplar dataset construction method is proposed based on multiple-size partition strategy to lower calculation cost of exemplar matching. Second, a matching criterion based on gradient information and entropy correlation coefficient is also proposed to enhance the matching accuracy. Third, L0.5-norm is introduced as the regularization item to maintain the sparsity of blur kernel. Experiments verify the superiority of the proposed approaches and extensive evaluations against state-of-the-art methods demonstrate the effectiveness of the proposed algorithm.

Nowadays, with the rapid growth of imaging and social network, huge volumes of image data are produced and shared on social media. Social image annotation has been an important and challenging task in the fields of computer vision and machine learning, which can facilitate large-scale image retrieval, indexing, and management. The four most challenges of social image annotation are semantic gap, tag refinement, label-imbalance, and annotation efficiency. To address these issues, we propose an efficient and effective annotation method based on the Mean of Positive Examples (MPE) corresponding to each label. First, we refine user-provided noisy tags by our proposed local smoothing process, and consider the refined tags as key features in contrast to the previous methods that consider them as side information, which significantly improves annotation performance. Second, we propose a weighted trans-media similarity measure method that fuses all modality information in identifying proper neighbors, which promotes the semantic level and eases image annotation. Third, our MPE model gives equal importance to all labels, thus, improving the annotation performance of infrequent labels without sacrificing that of frequent labels. Fourth, our MPE model can dramatically decrease space-time overheads, since the time cost of annotating an image is unaffected by the size of the training image dataset, but relying on the size of label vocabulary. Therefore, our proposed method can be applied to real-world large-scale online social image repositories. Extensive experiments on both benchmark datasets demonstrate the effectiveness and efficiency of our MPE model.