3D Interactive Visualization using Image Color Distribution

  • Authors

    • Chaelin Lee
    • Sanghyun Seo
    https://doi.org/10.14419/ijet.v7i3.24.22825
  • Information Visualization, Color Distribution, 3D Visualization, Color Model, Info-Graphic

  • Abstract

    Background/Objectives: Technologies related to image processing such as transforming the atmosphere of images or adding effects to images have been making rapid progress owing to the recent advancement of media.

    Methods/Statistical analysis: We need to devise methods to easily identify color composition and distribution in 3D space. This study introduces a method of visualizing the color distribution in 3D using standard color models so that the distribution pattern of color information in images can be easily understood.

    Findings: The distribution of colors that make up these images provides people with various stimuli and cognitive information. In order to convert images according to the user's intention in image manipulation research, the process of analyzing the images is very important, yet it is also significant to confirm that they have been converted as intended.

    Improvements/Applications: Our proposed method enables the user to intuitively understand and recognize color information of image.

     

     

  • References

    1. [1] Ware, C. (2012). Information Visualization, Third Edition: Perception for Design (Interactive Technologies) 3rd Edition, Morgan Kaufman.

      [2] Evergreen, S. (2016). Effective Data Visualization: The Right Chart for the Right Data, SAGE Publications Inc.

      [3] Gonzalez, R. C.,& Woods, R. E. (2017). Digital Image Processing 4th edition, Pearson.

      [4] Liu, S., Cui, W., Wu, Y., & Liu, M. (2014). A survey on information visualization: recent advances and challenges. The Visual Computer, 20(12), 1373-1393.

      [5] Moreland, K., (2013). A survey of visualization pipelines. IEEE Transactions on Visualization and Computer Graphics, 19(3), 367-378.

      [6] Chi, E.H.H., (2000). A taxonomy of visualization techniques using the data state reference model. Proceeding of the IEEE Symposium on Information Visualization InforVis, 9-10.

      [7] Moere, A.V., Tomitsch, M., Wimmer, C.,Bösch,C., & Grechenig, T. (2012). Evaluating the effect of style in information visualization. IEEE Transactions on Visualization and Computer Graphics, 18(12), 2739–2748.

      [8] Geng, Z., Peng, Z., Laramee, R.S., Roberts, J.C., &Walker, R. (2011). Angular histograms: frequency-based visualizations for large, high dimensional data. IEEE Transactions on Visualization and Computer Graphics, 17(12),2572–2580.

      [9] Wee, M. C.(2017). An improved diversity visualization system for multivariate data. Journal of Visualziation, 20(1), 163-179

      [10] Robertson, G., Card, S., & Mackinlay, J. (1993). Information Visualization Using 3D Interactive Animation. Communications of the ACM, 36(4), 56–71.

      [11] Christel, M., Olligschlaeger, A.,& Huang, C. (2000). Interactive maps for a digital video library, IEEE Multimedia, 7(1), 60–67.

      [12] Chen, T., Lu, A., &Hu, S.-M. (2012). Visual storylines: semantic visualization of movie sequence. Computer & Graphics, 36(4), 241–249.

      [13] Pretorius, A.J., Bray, M.-A., Carpenter, A.E., & Ruddle, R.A. (2011). Visualization of parameter space for image analysis. IEEE Transactions on Visualization and Computer Graphics, 17(12), 2402–2411

      [14] Colombo, C., Del Bimbo, A., & Pala, P. (1999). Semantics in visual information retrieval, IEEE Multimedia, 6(3), 38–53.

  • Downloads

  • How to Cite

    Lee, C., & Seo, S. (2018). 3D Interactive Visualization using Image Color Distribution. International Journal of Engineering & Technology, 7(3.24), 609-612. https://doi.org/10.14419/ijet.v7i3.24.22825

    Received date: 2018-12-02

    Accepted date: 2018-12-02