An intuitive model of perceptual grouping for HCI design

ABSTRACT

Understanding and exploiting the abilities of the human visual system is an important part of the design of usable user interfaces and information visualizations. Good design enables quick, easy and veridical perception of key components of that design. An important facet of human vision is its ability to seemingly effortlessly perform "perceptual organization; it transforms individual feature estimates into perception of coherent regions, structures, and objects. We perceive regions grouped by proximity and feature similarity, grouping of curves by good continuation, and grouping of regions of coherent texture. In this paper, we discuss a simple model for a broad range of perceptual grouping phenomena. It takes as input an arbitrary image, and returns a structure describing the predicted visual organization of the image. We demonstrate that this model can capture aspects of traditional design rules, and predicts visual percepts in classic perceptual grouping displays.

REFERENCES

• 1: Wertheimer, M. Laws of Organization in Perceptual Forms. Harcourt Brace Jovanovich, London, 1938.
• 2: Koffka, K. Principles of Gestalt Psychology. Hartcourt, New York, 1935.
• 3: Colin Ware, Information Visualization: Perception for Design, Morgan Kaufmann Publishers Inc., San Francisco, CA, 2004
• 4: Kosslyn, S. M. Understanding charts and graphs. Applied Cognitive Psychology, 3 (1989), 185--225.
• 5: Edward R. Tufte, The visual display of quantitative information, Graphics Press, Cheshire, CT, 1986
• 6: William S. Cleveland, The elements of graphing data, Wadsworth Publ. Co., Belmont, CA, 1985
• 7: Jock Mackinlay, Applying a theory of graphical presentation to the graphic design of user interfaces, Proceedings of the 1st annual ACM SIGGRAPH symposium on User Interface Software, p.179-189, October 17-19, 1988, Alberta, Canada [doi>10.1145/62402.62431]
• 8: Tullis, T. S. A computer-based tool for evaluating alphanumeric displays. INTERACT 84 (1984), 719--723.
• 9: Vincent, L. Current topics in applied morphological image analysis. In Kendall, W. S. et al., eds., Current Trends in Stochastic Geometry and Its Applications. Chapman&Hall, 1997.
• 10: Shneiderman, B., Chimera, R., Jog, N., Stimart, R.,&White, D. Evaluating spatial and texture style of displays. In MacDonald, L. W. and Lowe, A. C., eds., Display Systems: Design and Applications. John Wiley&Sons, Chichester, U.K., 1997.
• 11: E. Saund, Symbolic Construction of a 2-D Scale-Space Image, IEEE Transactions on Pattern Analysis and Machine Intelligence, v.12 n.8, p.817-830, August 1990 [doi>10.1109/34.57672]
• 12: Healey, C.G., Booth, K.S.,&Enns, J.T. Harnessing preattentive processes for multivariate data visualization. Proc. Graphics Interface '93 (1993), 107--117.
• 13: Jianbo Shi , Jitendra Malik, Normalized Cuts and Image Segmentation, Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97), p.731, June 17-19, 1997
• 14: Field, D. J., Hayes, A.,&Hess, R. F. Contour integration by the human visual system: evidence for a local association field. Vision Research, 33 (1993), 173--193.
• 15: P. Parent , S. W. ucker, Trace Inference, Curvature Consistency, and Curve Detection, IEEE Transactions on Pattern Analysis and Machine Intelligence, v.11 n.8, p.823-839, August 1989 [doi>10.1109/34.31445]
• 16: Mumford, D. Elastica and computer vision. In Algebraic Geometry and Its Applications. Springer-Verlag, NY, 1994.
• 17: Grossberg, S.&Mingolla, E. Neural dynamics of perceptual grouping: textures, boundaries, and emergent segmentations. Perception&Psychophysics, 38 (1985), 141--171.
• 18: Richard O. Duda , Peter E. Hart , David G. Stork, Pattern Classification (2nd Edition), Wiley-Interscience, 2000
• 19: Ohlander, R., Price, K.,&Reddy, R. Picture segmentation by a recursive region splitting method. Computer Graphics and Image Processing, 8 (1978), 313--333.
• 20: Witkin, A. P. Scale-space filtering. Proc. 8th Int. Conf. on Artificial Intelligence (1983), 1019--1022.
• 21: Koenderink, J. J. The structure of images. Biological Cybernetics, 50 (1984), 363--370.
• 22: Tony Lindeberg, Scale-Space Theory in Computer Vision, Kluwer Academic Publishers, Norwell, MA, 1994
• 23: Martin Wattenberg , Danyel Fisher, Analyzing perceptual organization in information graphics, Information Visualization, v.3 n.2, p.123-133, June 2004 [doi>10.1057/palgrave.ivs.9500070]
• 24: Palmer, S. E. Hierarchical structure in perceptual representation. Cognitive Psychology, 9 (1977), 441--474.
• 25: Navon, D. Forest before trees: The precedence of global features in visual perception. Cognitive Psychology, 9 (1977), 353--383.
• 26: David R. Martin , Charless C. Fowlkes , Jitendra Malik, Learning to Detect Natural Image Boundaries Using Local Brightness, Color, and Texture Cues, IEEE Transactions on Pattern Analysis and Machine Intelligence, v.26 n.5, p.530-549, May 2004 [doi>10.1109/TPAMI.2004.1273918]
• 27: Malik, J.&Perona, P. A computational model of texture segmentation. Proc. Computer Vision and Pattern Recognition (1989), 326--332.
• 28: Ruth Rosenholz, Significantly Different Textures: A Computational Model of Pre-attentive Texture Segmentation, Proceedings of the 6th European Conference on Computer Vision-Part II, p.197-211, June 26-July 01, 2000
• 29: P. Perona , J. Malik, Scale-Space and Edge Detection Using Anisotropic Diffusion, IEEE Transactions on Pattern Analysis and Machine Intelligence, v.12 n.7, p.629-639, July 1990 [doi>10.1109/34.56205]
• 30: Geman, S.&Geman, D. Stochastic relaxation, Gibbs distributions, and the Bayesian restoration of images. IEEE Trans. Pattern Analysis&Machine Intelligence, 6 (1984), 721--741.
• 31: C. Tomasi , R. Manduchi, Bilateral Filtering for Gray and Color Images, Proceedings of the Sixth International Conference on Computer Vision, p.839, January 04-07, 1998
• 32: Frédo Durand , Julie Dorsey, Fast bilateral filtering for the display of high-dynamic-range images, ACM Transactions on Graphics (TOG), v.21 n.3, July 2002
• 33: Paris, S.&Durand, F. A fast approximation of the bi-lateral filter using a signal processing approach. Proc. European Conf. Computer Vision (2006), 568--580.
• 34: Julesz, B. A theory of preattentive texture discrimination based on the first order statistics of textons. Biological Cybernetics, 41 (1981), 131--138.
• 35: Beck, J. Textural segmentation, second-order statistics,&textural elements. Biol. Cybern., 48 (1983), 125--130.
• 36: Voorhees, H.&Poggio, T. Computing texture boundaries from images. Nature, 333 (1988), 364--367.
• 37: Li, Z. Pre-attentive segmentation in the primary visual cortex. Spatial Vision, 13 (2000), 25--50.
• 38: Helmholtz, H. Handbook of Physiological Optics. Vol. 3, The Perceptions of Vision. Optical Society of America, Rochester, 1925.
• 39: Paris, S.&Durand, F. A topological approach to hierarchical segmentation using mean shift. IEEE Conf. Computer Vision&Pattern Recognition (2007), 1--8.
• 40: Mark A. Ruzon , Carlo Tomasi, Edge, Junction, and Corner Detection Using Color Distributions, IEEE Transactions on Pattern Analysis and Machine Intelligence, v.23 n.11, p.1281-1295, November 2001 [doi>10.1109/34.969118]
• 41: Geisler, W. S., Perry, J. S., Super, B. J.,&Gallogly, D. P. Edge co-occurrence in natural images predicts contour grouping performance. Vision Research, 41 (2001), 711--724.
• 42: Francisco J. Estrada , James H. Elder, Multi-Scale Contour Extraction Based on Natural Image Statistics, Proceedings of the 2006 Conference on Computer Vision and Pattern Recognition Workshop, p.183, June 17-22, 2006 [doi>10.1109/CVPRW.2006.134]
• 43: Schinkel-Bielefeld, N. Contour integration models predicting human behavior. University of Bremen, 2007. http://nbn-resolving.de/urn:nbn:de:gbv:46diss000-108845.
• 44: Shyjan Mahamud , Lance R. Williams , Karvel K. Thornber , Kanglin Xu, Segmentation of Multiple Salient Closed Contours from Real Images, IEEE Transactions on Pattern Analysis and Machine Intelligence, v.25 n.4, p.433-444, April 2003 [doi>10.1109/TPAMI.2003.1190570]
• 45: Ren, X., Fowlkes, C.,&Malik, J. Figure/ground assignment in natural images. Proc. European Conf. on Computer Vision (2006), 614--627.
• 46: C.I.E. Recommendations on uniform color spaces, color difference equations, psychometric color terms. Supp. No. 2 to CIE publ. 15 (E.-1.3.1) 1971/(TC-1.3.) (1978).
• 47: Dorin Comaniciu , Peter Meer, Mean Shift: A Robust Approach Toward Feature Space Analysis, IEEE Transactions on Pattern Analysis and Machine Intelligence, v.24 n.5, p.603-619, May 2002 [doi>10.1109/34.1000236]
• 48: William T. Freeman , Edward H. Adelson, The Design and Use of Steerable Filters, IEEE Transactions on Pattern Analysis and Machine Intelligence, v.13 n.9, p.891-906, September 1991 [doi>10.1109/34.93808]
• 49: Landy, M. S.&Bergen, J. R. Texture segregation and orientation gradient. Vis. Research, 31 (1991), 679--691.
• 50: Marr, D. and Hildreth, E. C. Theory of edge detection. Proc. Royal Society, London B, 207 (1980), 187--217.
• 51: Logan, G. The CODE theory of visual attention: An integration of space-based and object-based attention. Psychological Review, 103, 4 (1996), 603--649.
• 52: Watt, R., Ledgeway, T., and Dakin, S. C. Families of models for Gabor paths demonstrate the importance of spatial adjacency. Journal of Vision, 8, 7 (2008), 1--19. htp://journalofvision.org/8/7/23/.

INDEX TERMS

Primary Classification:
  H. Information Systems
  H.5 INFORMATION INTERFACES AND PRESENTATION (I.7)
      H.5.2 User Interfaces (D.2.2, H.1.2, I.3.6)
          Subjects: Theory and methods

General Terms:
Algorithms, Design, Human Factors, Theory

Keywords:
contour integration, gestalt, good continuation, grouping, perceptual organization, proximity, similarity