In this work, a fast Bilateral Solver for Quality Estimation Based multi-focus Image Fusion method (BS-QEBIF) is proposed. The all-in-focus image is generated by pixel-wise summing up the multi-focus source images with their focus-levels maps as weights. Since the visual quality of an image patch is highly correlated with its focus level, the focus-level maps are preliminarily obtained based on visual quality scores, as pre-estimations. However, the pre-estimations are not ideal. Thus the fast bilateral solver is then adopted to smooth the pre-estimations, and edges in the multi-focus source images can be preserved simultaneously. The edge-preserving smoothed results are utilized as final focus-level maps. Moreover, this work provides a confidence-map solution for the unstable fusion in the focus-level-changed boundary regions. Experiments were conducted on @math pairs of source images. The proposed BS-QEBIF outperforms the other @math fusion methods objectively and subjectively. The all-in-focus image produced by the proposed method can well maintain the details in the multi-focus source images and does not suffer from any residual errors. Experimental results show that BS-QEBIF can handle the focus-level-changed boundary regions without any blocking, ringing and blurring artifacts.
In the last decades, neural network and learning based methods were widely employed in image processing tasks and achieved impressive success. Some image fusion methods also try to utilize machine learning to help focus level estimation. For example, some methods @cite_25 @cite_27 @cite_26 @cite_50 adopt pulse coupled neural networks (PCNN) to do image fusion where PCNN does not require the training procedure. Some other methods @cite_47 @cite_56 @cite_31 train neural networks or dictionaries where focus level estimation was regarded as a classification problem to divide a local region into an in-focus or out-of-focus one. To do the training, @cite_56 @cite_31 collect some all-in-focus images and manually blur them using the Gaussian filters, while @cite_25 manually labels in-focus out-of-focus regions from source images as training sets.
@cite_31 @cite_26 @cite_56 @cite_27 @cite_50 @cite_47 @cite_25
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A citing sentence is one that appears in a scientific article and cites previous work. Citing sentences have been studied and used in many applications. For example, they have been used in scientific paper summarization, automatic survey generation, paraphrase identification, and citation function classification. Citing sentences that cite multiple papers are common in scientific writing. This observation should be taken into consideration when using citing sentences in applications. For instance, when a citing sentence is used in a summary of a scientific paper, only the fragments of the sentence that are relevant to the summarized paper should be included in the summary. In this paper, we present and compare three different approaches for identifying the fragments of a citing sentence that are related to a given target reference. Our methods are: word classification, sequence labeling, and segment classification. Our experiments show that segment classification achieves the best results. Abstract Documents in computer-readable form can be used to provide information about other documents, i.e. those they cite. To do this efficiently requires procedures for computer recognition of citing statements. This is not easy, especially for multi-sentence citing statements. Computer recognition procedures have been developed which are accurate to the following extent: 73 of the words in statements selected by computer procedures as being citing statements are words which are correctly attributable to the corresponding documents. The retrieval effectiveness of computer-recognized citing statements was tested in the following way. First, for eight retrieval requests in inorganiic chemistry, average recall by search of Chemical Abstracts Service indexing and Chemical Abstracts abstract text words was found to be 50 . Words from citing statements referring to the papers to be retrieved were then added to the index terms and abstract words as additional access points, and searching was repeated. Average recall increased to 70 . Only words from citing statements published within a year of the cited papers were used. The retrieval effect of citing statement words alone (published within a year) without index or abstract terms was the following: average recall was 40 . When just the words of the titles of the cited papers were added to those citing statement words, average recall increased to 50 . This paper proposes a new method for multi-focus image fusion based on dual-channel pulse coupled neural networks (dual-channel PCNN). Compared with previous methods, our method does not decompose the input source images and need not employ more PCNNs or other algorithms such as DWT. This method employs the dual-channel PCNN to implement multi-focus image fusion. Two parallel source images are directly input into PCNN. Meanwhile focus measure is carried out for source images. According to results of focus measure, weighted coefficients are automatically adjusted. The rule of auto-adjusting depends on the specific transformation. Input images are combined in the dual-channel PCNN. Four group experiments are designed to testify the performance of the proposed method. Several existing methods are compared with our method. Experimental results show our presented method outperforms existing methods, in both visual effect and objective evaluation criteria. Finally, some practical applications are given further. The old Asian legend about the blind men and the elephant comes to mind when looking at how different authors of scientific papers describe a piece of related prior work. It turns out that different citations to the same paper often focus on different aspects of that paper and that neither provides a full description of its full set of contributions. In this article, we will describe our investigation of this phenomenon. We studied citation summaries in the context of research papers in the biomedical domain. A citation summary is the set of citing sentences for a given article and can be used as a surrogate for the actual article in a variety of scenarios. It contains information that was deemed by peers to be important. Our study shows that citation summaries overlap to some extent with the abstracts of the papers and that they also differ from them in that they focus on different aspects of these papers than do the abstracts. In addition to this, co-cited articles (which are pairs of articles cited by another article) tend to be similar. We show results based on a lexical similarity metric called cohesion to justify our claims. © 2008 Wiley Periodicals, Inc.