Beginning Chinese Reader by John DeFrancis

By John DeFrancis

This moment variation, just like the prior first version, introduces a few of the major types of chinese language as stumbled on earlier than and after the institution of the People's Republic of China in 1949. whereas carrying on with to emphasize the fundamental value of the conventional usages, equivalent to the ordinary characters to be present in all fabrics released ahead of the adoption of the simplified types in 1956 and nonetheless in use in a few parts, the current revision is going additional in contrasting version usages and in offering extra fabric relevent to the PRC.

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3. The precision-recall curves contrast of each step based on MSRA-5000 dataset for the proposed algorithm. Fig. 4. Comparison of saliency maps in different methods. Qualitative Comparison with Other Algorithms. The proposed algorithm is then evaluated against some state-of-the-art algorithms, including SR [6], FT [10], HC [11], UFO [7], AMC [12], MR [16], and RRWR [1]. These results are got by running open codes provided by their authors and Fig. 4 shows their qualitative comparison. Results generated by the proposed GMSL highlight the salient object well with less noise, because this algorithm combines both the graph-based model and statistical learning, where the former one is effective picking up the boundary prior information and the latter one is effective finding out the distribution regularities of foreground and background.

Moreover, our method tends to generate a clearer result of image details, as illustrated in Fig. 10. These benefits from the incorporation of a filter bank into image dehazing. , jump edges and corners. Single Image Haze Removal Based on Priori Image Geometry and Edge-Preserving 37 Fig. 10. Comparison with He’s work. Left: input image. Middle: He’s result. Right: our result. (Color figure online) Figures 11 and 12 allow the comparison of our results with four state of the art visibility restoration algorithms: Tan [7], Fattal [6], Tarel [9] and He [8].

10-GHz Intel® CoreTM i5-2400 CPU. 1 Qualitative Comparison Figure 6 illustrates some examples of our dehazing results and the recovered scene transmission functions. As can be seen from the results, our method can recover rich Single Image Haze Removal Based on Priori Image Geometry and Edge-Preserving 35 Fig. 6. Image dehazing results by our method. Top: input haze images. Middle: the dehazing results. Bottom: the recovered transmission functions. The recovered transmission gives an estimation of the density map of hazes in the input image.

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