skip to main content
10.1145/2254556.2254597acmotherconferencesArticle/Chapter ViewAbstractPublication PagesaviConference Proceedingsconference-collections
research-article

GravNav: using a gravity model for multi-scale navigation

Published:21 May 2012Publication History

ABSTRACT

We present gravity navigation (GravNav), a family of multi-scale navigation techniques that use a gravity-inspired model for assisting navigation in large visual 2D spaces based on the interest and salience of visual objects in the space. GravNav is an instance of topology-aware navigation, which makes use of the structure of the visual space to aid navigation. We have performed a controlled study comparing GravNav to standard zoom and pan navigation, with and without variable-rate zoom control. Our results show a significant improvement for GravNav over standard navigation, particularly when coupled with variable-rate zoom. We also report findings on user behavior in multi-scale navigation.

References

  1. D. Ahlström, M. Hitz, and G. Leitner. An evaluation of sticky and force enhanced targets in multi target situations. In Proceedings of the ACM Nordic Conference on Human-Computer Interaction, pages 58--67, 2006. Google ScholarGoogle ScholarDigital LibraryDigital Library
  2. C. Appert and J.-D. Fekete. OrthoZoom scroller: 1D multi-scale navigation. In Proceedings of the ACM Conference on Human Factors in Computing Systems, pages 21--30, 2006. Google ScholarGoogle ScholarDigital LibraryDigital Library
  3. P. Baudisch and R. Rosenholtz. Halo: a technique for visualizing off-screen objects. In Proceedings of the ACM Conference on Human Factors in Computing Systems, pages 481--488, 2003. Google ScholarGoogle ScholarDigital LibraryDigital Library
  4. R. Blanch, Y. Guiard, and M. Beaudouin-Lafon. Semantic pointing: improving target acquisition with control-display ratio adaptation. In Proceedings of ACM Conference on Human Factors in Computing Systems, pages 519--526, 2004. Google ScholarGoogle ScholarDigital LibraryDigital Library
  5. R. Desimone and J. Duncan. Neural mechanisms of selective visual attention. Annual Review of Neuroscience, 18:193--222, 1995.Google ScholarGoogle ScholarCross RefCross Ref
  6. N. Elmqvist and J.-D. Fekete. Semantic pointing for object picking in complex 3D environments. In Proceedings of the Graphics Interface Conference, pages 243--250, 2008. Google ScholarGoogle ScholarDigital LibraryDigital Library
  7. N. Elmqvist, M. E. Tudoreanu, and P. Tsigas. Evaluating motion constraints for 3D wayfinding in immersive and desktop virtual environments. In Proceedings of the ACM Conference on Human Factors in Computing Systems, pages 1769--1778, 2008. Google ScholarGoogle ScholarDigital LibraryDigital Library
  8. D. Fisher. Hotmap: Looking at geographic attention. IEEE Transactions on Visualization and Computer Graphics, 13(6):1184--1191, 2007. Google ScholarGoogle ScholarDigital LibraryDigital Library
  9. P. M. Fitts. The information capacity of the human motor system in controlling the amplitude of movement. Journal of Experimental Psychology, 47:381--391, 1954.Google ScholarGoogle ScholarCross RefCross Ref
  10. G. W. Furnas. Effective view navigation. In Proceedings of the ACM Conference on Human Factors in Computing Systems, pages 367--374, 1997. Google ScholarGoogle ScholarDigital LibraryDigital Library
  11. G. W. Furnas and B. B. Bederson. Space-scale diagrams: Understanding multiscale interfaces. In Proceedings of the ACM Conference on Human Factors in Computing Systems, pages 234--241, 1995. Google ScholarGoogle ScholarDigital LibraryDigital Library
  12. T. A. Galyean. Guided navigation of virtual environments. In Proceedings of the ACM Symposium on Interactive 3D Graphics, pages 103--104, 1995. Google ScholarGoogle ScholarDigital LibraryDigital Library
  13. S. Ghani, N. H. Riche, and N. Elmqvist. Dynamic insets for context-aware graph navigation. Computer Graphics Forum, 30(3):861--870, 2011. Google ScholarGoogle ScholarDigital LibraryDigital Library
  14. Y. Guiard, M. Beaudouin-Lafon, J. Bastin, D. Pasveer, and S. Zhai. View size and pointing difficulty in multi-scale navigation. In Proceedings of the ACM Conference on Advanced Visual Interfaces, pages 117--124, 2004. Google ScholarGoogle ScholarDigital LibraryDigital Library
  15. T. Igarashi and K. Hinckley. Speed-dependent automatic zooming for browsing large documents. In Proceedings of the ACM Symposium on User Interface Software and Technology, pages 139--148, 2000. Google ScholarGoogle ScholarDigital LibraryDigital Library
  16. E. W. Ishak and S. Feiner. Content-aware scrolling. In Proceedings of the ACM Symposium on User Interface Software and Technology, pages 155--158, 2006. Google ScholarGoogle ScholarDigital LibraryDigital Library
  17. L. Itti, C. Koch, and E. Niebur. A model of saliency-based visual attention for rapid scene analysis. IEEE Transactions on Pattern Analysis and Machine Intelligence, 20:1254--1259, 1998. Google ScholarGoogle ScholarDigital LibraryDigital Library
  18. S. Jul and G. W. Furnas. Critical zones in desert fog: Aids to multiscale navigation. In Proceedings of the ACM Symposium on User Interface Software and Technology, pages 97--106, 1998. Google ScholarGoogle ScholarDigital LibraryDigital Library
  19. S. MacKenzie. Fitts' law as a research and design tool in human-computer interaction. Human-Computer Interaction, 7:91--139, 1992. Google ScholarGoogle ScholarDigital LibraryDigital Library
  20. R. L. Mandryk and C. Gutwin. Perceptibility and utility of sticky targets. In Proceedings of the Graphics Interface Conference, pages 65--72, 2008. Google ScholarGoogle ScholarDigital LibraryDigital Library
  21. T. Moscovich, F. Chevalier, N. Henry, E. Pietriga, and J.-D. Fekete. Topology-aware navigation in large networks. In Proceedings of the ACM Conference on Human Factors in Computing Systems, pages 2319--2328, 2009. Google ScholarGoogle ScholarDigital LibraryDigital Library
  22. K. Perlin and D. Fox. Pad: An alternative approach to the computer interface. In Computer Graphics (ACM SIGGRAPH '93 Proceedings), pages 57--64, 1993. Google ScholarGoogle ScholarDigital LibraryDigital Library
  23. A. M. Treisman and G. Gelade. A feature-integration theory of attention. Cognitive Psychology, 12:97--136, 1980.Google ScholarGoogle ScholarCross RefCross Ref
  24. J. J. van Wijk and W. A. A. Nuij. Smooth and efficient zooming and panning. In Proceedings of the IEEE Symposium on Information Visualization, pages 15--22, 2003. Google ScholarGoogle ScholarDigital LibraryDigital Library
  25. M. Verm and P. W. McOwan. A semi-automated approach to balancing of bottom-up salience for predicting change detection performance. Journal of Vision, 10(6):3, 2010.Google ScholarGoogle ScholarCross RefCross Ref
  26. D. Walther, U. Rutishauser, C. Koch, and P. Perona. Selective visual attention enables learning and recognition of multiple objects in cluttered scenes. Computer Vision and Image Understanding, 100(1--2):41--63, 2005. Google ScholarGoogle ScholarDigital LibraryDigital Library
  27. A. Worden, N. Walker, K. Bharat, and S. Hudson. Making computers easier for older adults to use: Area cursors and sticky icons. In Proceedings of the ACM Conference on Human Factors in Computing Systems, pages 266--271, 1997. Google ScholarGoogle ScholarDigital LibraryDigital Library

Index Terms

  1. GravNav: using a gravity model for multi-scale navigation

        Recommendations

        Comments

        Login options

        Check if you have access through your login credentials or your institution to get full access on this article.

        Sign in
        • Published in

          cover image ACM Other conferences
          AVI '12: Proceedings of the International Working Conference on Advanced Visual Interfaces
          May 2012
          846 pages
          ISBN:9781450312875
          DOI:10.1145/2254556

          Copyright © 2012 ACM

          Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

          Publisher

          Association for Computing Machinery

          New York, NY, United States

          Publication History

          • Published: 21 May 2012

          Permissions

          Request permissions about this article.

          Request Permissions

          Check for updates

          Qualifiers

          • research-article

          Acceptance Rates

          Overall Acceptance Rate128of490submissions,26%

        PDF Format

        View or Download as a PDF file.

        PDF

        eReader

        View online with eReader.

        eReader