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Drawing and studying on histogram

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Abstract

Statistical graphics has three important features -intuition, vivid and lively. It is an important tool in exploratory analysis. Histogram is an important histogram. People can use its intuitionistic images to show the distribution of numbers to get the regularity of data distribution. Histogram is widely used in shape matching, imagine retrieval, feature matching and visual tracking etc. It can be helpful for the subsequent data analysis as well. This paper discusses the mathematical principle, practical principle and the operation method of histogram. Also histograms have been drawn in this article based on data of the Old Faithful Geyser of California. R-project is used to draw the histogram to display the eruption time of Old Faithful. The default histogram of R-project, the probability density compound histogram and multi-interval histogram have been drawn by using R-program and at the meantime, the R-project program and the three imagine results are supplied and the author did the comparative analysis of them.

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References

  1. Abdalhaq, B., Luque, E.: Between classical and ideal: enhancing wildland fire prediction using cluster computing. Clust. Comput. 9(3), 329–343 (2006)

    Article  Google Scholar 

  2. Zhou, Y., Liu, J.T., Bai, X.: Research and perspective on shape matching. Acta Autom. Sin. 38(6), 889–910 (2012)

    Article  MathSciNet  Google Scholar 

  3. Yang, H.J., Jian, X.Y., Cao, F.Y., et al.: Image retrieval method based on combined Euler vector and edges direction histogram. Pattern Recognit. Artif. Intell. 25(3), 450–455 (2012)

    Google Scholar 

  4. Yu, W.S., Hou, Z.Q., Tian, X.H.: Non-rigid object tracking using joint matching of local features. J. Xidian Univ. 41, 183–189 (2014)

    Google Scholar 

  5. Wang, Y., Chen, F.X., Guo, H.X.: Kernel spatial histogram target tracking based on template drift correction. Acta Autom. Sin. 38, 430–436 (2012)

    Article  Google Scholar 

  6. Karavasilis, V., Nikou, C., Likas, A.: Visual tracking using the earth movers distance between gaussian mixtures and Kalman filtering. Image Vis. Comput. 29, 295–305 (2011)

    Article  Google Scholar 

  7. Chen, L., Ding, G.H., Guo, L.: Image thresholding based on mutual recognition of histogram. J. Infrared Millim. Waves 30, 80–84 (2011)

    Article  Google Scholar 

  8. Rice, J., Learning, C.: Mathematical statistics and data analysis with cd data sets. Math. Gaz. 72(72), 390–391 (2006)

    Google Scholar 

  9. Ye, J., Xu, Z., Ding, Y.: Secure outsourcing of modular exponentiations in cloud and cluster computing. Clust. Comput. 19(2), 811–820 (2016)

    Article  Google Scholar 

  10. Patil, P., Bagkavos, D.: Histogram for hazard rate estimation. Sankhya B 74(2), 286–301 (2012)

    Article  MathSciNet  Google Scholar 

  11. Engel, J.: The multiresolution histogram. Metrika 46(1), 41–57 (1997)

    Article  MathSciNet  Google Scholar 

  12. Xu, X., Guo, M.Z., Shi, F.L.: The simulation of two peak distribution. J. Yunnan Normal Univ. 33(2), 46–51 (2013)

    Google Scholar 

  13. Urbanek, S.: iPlots eXtreme: next-generation interactive graphics design and implementation of modern interactive graphics. Comput. Statistics 26(3), 381–393 (2011)

    Article  MathSciNet  Google Scholar 

  14. Yu, W.S., Tian, X.H., Hou, Z.Q., et al.: Improved quadratic-form metric for histogram distance measurement. J. Xidian Univ. 42, 161–167 (2015)

    Google Scholar 

  15. Leichter, I.: Mean shift trackers with cross-bin metrics. IEEE Trans. Pattern Anal. Mach. Intell. 34, 695–706 (2012)

    Article  Google Scholar 

  16. Huang, Y., Fan, C., Zhu, Q.P., et al.: HOG-LBP pedestrian detection. Opt. Precis. Eng. 21, 1047–1053 (2013)

    Article  Google Scholar 

  17. Dalal, Navneet, Triggs, Bill: Histograms of oriented gradients for human detection. IEEE Conf. Comput. Vis. Pattern Recognit. 1, 886–893 (2005)

    Google Scholar 

  18. Wang, Y., Li, J., Wang, H.H.: Cluster and cloud computing framework for scientific metrology in flow control. Clust. Comput. 1, 1–10 (2017)

    Google Scholar 

  19. Song, X., Liu, F., Luo, X., et al.: Steganalysis of perturbed quantization steganography based on the enhanced histogram features. Multimed. Tools Appl. 74(24), 11045–11071 (2015)

    Article  Google Scholar 

  20. Yang, Y., Liu, Y.X., Dong, Q.F.: Sliced integral histogram: an efficient histogram computing algorithm and its FPGA implementation. Multimed. Tools Appl. 76, 1–18 (2017)

    Article  Google Scholar 

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Acknowledgements

The authors acknowledge the National Natural Science Foundation of China (Grant: 61375066), the National Natural Science Foundation of China (Grant: 11471051).

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Correspondence to Yajie Li.

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Li, Y., Zhang, Y., Yu, M. et al. Drawing and studying on histogram. Cluster Comput 22 (Suppl 2), 3999–4006 (2019). https://doi.org/10.1007/s10586-018-2606-0

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  • DOI: https://doi.org/10.1007/s10586-018-2606-0

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