This Article 
   
 Share 
   
 Bibliographic References 
   
 Add to: 
 
Digg
Furl
Spurl
Blink
Simpy
Google
Del.icio.us
Y!MyWeb
 
 Search 
   
Using Perceptual Organization to Extract 3D Structures
November 1989 (vol. 11 no. 11)
pp. 1121-1139

The authors describe an approach to perceptual grouping for detecting and describing 3-D objects in complex images and apply it to the task of detecting and describing complex buildings in aerial images. They argue that representations of structural relationships in the arrangements of primitive image features, as detected by the perceptual organization process, are essential for analyzing complex imagery. They term these representations collated features. The choice of collated features is determined by the generic shape of the desired objects in the scene. The detection process for collated features is more robust than the local operations for region segmentation and contour tracing. The important structural information encoded in collated features aids various visual tasks such as object segmentation, correspondence processes, and shape description. The proposed method initially detects all reasonable feature groupings. A constraint satisfaction network is then used to model the complex interactions between the collations and select the promising ones. Stereo matching is performed on the collations to obtain height information. This aids in further reasoning on the collated features and results in the 3-D description of the desired objects.

[1] T. O. Binford, "Survey of model based image analysis systems,"Int. J. Robotics Res., vol. 1, no. 1, 1982.
[2] D. Lowe, Perceptual Organization And Visual Recognition. Boston: Kluwer, 1985.
[3] D. G. Lowe and T. O. Binford, "Perceptual organization as a basis for visual recognition," inProc. AAAI-83, Washington, DC, Aug. 1983.
[4] A. P. Witkin and J. M. Tenenbaum, "On the role of structure in vision," inHuman and Machine Vision, Beck, Hope, and Rosenfeld, Eds. New York: Academic, 1983, pp. 481-543.
[5] S. E. Palmer, "The psychology of perceptual organization: A transformational approach," inHuman and Machine Vision, Beck, Hope, and Rosenfeld, Eds. New York: Academic, 1983, pp. 269-339.
[6] S. W. Zucker, "Computational and psychophysical experiments in grouping: Early orientation selection," inHuman and Machine Vision, Beck, Hope, and Rosenfeld, Eds. New York: Academic, 1983, pp. 545-567.
[7] R. E. Kelly, P. R. M. McConnell, and S. J. Mildenberger, "The Gestalt photomapping system,"J. Photogram. Eng. Remote Sensing, 1977.
[8] A. Triesman, "Perceptual grouping and attention in visual search for features and objects,"J. Exp. Psychol.: Human Perception Perform., vol. 8, no. 2, pp. 194-214, 1982.
[9] K. A. Stevens, "Computation of locally parallel structure,"Biol. Cybern., vol. 29, pp. 19-28, 1981.
[10] D. Katz,Gestalt Psychology: Its Nature and Significance. New York: Ronald Press, 1950.
[11] B. Julesz, "Figure and ground perception in briefly presented isodipole textures," inPerceptual Organization, Kubovy and Pomerantz, Eds. Hillsdale, NJ: Lawrence Erlbaum, 1981, pp. 27-54.
[12] F. Attneave, "Some informational aspects of visual perception,"Psychol. Rev., vol. 61, pp. 183-193, 1954.
[13] M.A. Fischler and R. C. Bolles, "Perceptual organization and curve partitioning,"IEEE Trans. Pattern Anal. Machine Intell., vol. PAMI-8, no. 1, pp. 100-105, 1986.
[14] R. Mohan and R. Nevatia, "Perceptual grouping with applications to 3D shape extraction," inProc. IEEE Comput. Soc. Workshop Computer Vision, Miami, FL, Dec. 1987.
[15] R. Mohan and R. Nevatia, "Perceptual grouping for the detection and description of structures in aerial images," inProc. DARPA Image Understanding Workshop, Cambridge, MA, Apr. 1988; also available as USC-IRIS Tech. Rep. 225.
[16] R. Nevatia and K. R. Babu, "Linear feature extraction and description,"Comput. Vision, Graphics, Image Processing, vol. 13, pp. 257-269, 1980.
[17] A. Huertas and R. Nevatia, "Detecting buildings in aerial images,"Comput. Vision Graphics Image Processing, vol. 41, pp. 131-152, 1988.
[18] A. Huertas and R. Nevatia, "Detection of buildings in aerial images using shape and shadows," inProc. IJCAI, Karlsruhe, W. Germany, Aug. 1983, pp. 1099- 1103.
[19] A. Huertas, R. Mohan, and R. Nevatia, "Detection of complex buildings in simple scenes," Inst. Robotics and Intelligent Systems, Univ. Southern California, Tech. Rep. IRIS 203, Sept. 1986.
[20] T. Matsuyama and V. Hwang, "SIGMA: A framework for image understanding: Intergration of bottom-up and top-down analyses," inProc. IJCAI, Los Angeles, CA, Aug. 1985.
[21] P. Fua and A. J. Hanson, "Using generic geometric models for intelligent shape extraction," inProc. DARPA Image Understanding Workshop, Los Angeles, CA, Feb. 1987.
[22] A. Hanson and R. E. Riseman,VISIONS: A Computer System for Interpreting Scenes. New York: Academic, 1978.
[23] D. M. McKeown, W. A. Harvey, and J. McDermott, "Rule-based interpretation of aerial imagery,"IEEE Trans. Pattern Anal. Machine Intell., vol. PAMI-7, pp. 570-585, 1985.
[24] D. H. Ballard and C. M. Brown,Computer Vision. Englewood Cliffs, NJ: Prentice-Hall, 1982.
[25] D. H. Ballard, "Form perception using transformation networks: Polyhedra," Dep. Comput. Sci., Univ. Rochester, Tech. Rep. TR 148, 1986.
[26] M. Herman and T. Kanade, "The 3D MOSAIC scene understanding system: Incremental reconstruction of 3D scenes from complex images," inProc. DARPA Image Understanding Workshop, 1984, pp. 137-148.
[27] G. Reynolds and J. R. Beveridge, "Searching for geometric structure in images of natural scenes," inProc. DARPA Image Understanding Workshop, Los Angeles, CA, Feb. 1987.
[28] D. H. Ballard, G. E. Hinton, and T. J. Sejnowski, "Parallel visual computation,"Nature, vol. 306, pp. 21-26, Nov. 1983.
[29] S. E. Fahlman and G. E. Hinton, "Connectionist architectures for artificial intelligence,"IEEE Comput. Mag., vol. 20, no. 1, pp. 100-109, 1987.
[30] S. Kirkpatrick, C. D. Gelatt, and M. P. Vecchi,"Optimization by simulated annealing,"Science, vol. 220, pp. 671-680, 1983.
[31] J. J. Hopfield and D. W. Tank, "Neural computation of decisions in optimization problems,"Biol. Cybern.vol. 52, pp. 141-152, 1985.
[32] J. J. Hopfield and D. W. Tank, "Computing with neural circuits: A model,"Science, vol. 233, pp. 625-633, 1986.
[33] D. E. Rumelhart, McClelland, and the PDP Research Group,Parallel Distributed Processing: Explorations in the Microstructures of Computing. Cambridge, MA: M.I.T. Press, 1986.
[34] S. E. Fahlman, G. E. Hinton, and T. J. Sejnowski, "Massively parallel architectures for AI: Netl, Thistle, and Boltzman machines," inProc. Nat. Conf. Artificial Intelligence, AAAI, Menlo Park, CA. Los Altos, CA: Kaufman, 1983.
[35] S. Geman and D. Geman, "Stochastic relaxation, Gibbs distributions, and the Bayesian restoration of images,"IEEE Trans. Pattern Anal. Machine Intell., vol. PAMI-6, pp. 721-741, Nov. 1984.
[36] J. A. Feldman and D. H. Ballard, "Connectionist models and their properties,"Cognitive Sci., vol. 6, pp. 205-254, 1982.
[37] J. J. Hopfield and D. W. Tank, "Neural networks and physical systems with emergent collective computational abilities,"Proc. Nat. Acad. Sci., vol. 79, pp. 2554-2558, Apr. 1982.
[38] J. J. Hopfield, "Neurons with graded response have collective computational properties like those of two-state neurons,"Proc. Nat. Acad. Sci., vol. 81, pp. 3088-3092, May 1984.
[39] R. A. Hummel and S. W. Zucker, "On the foundations of relaxation labeling process,"IEEE Trans. Pattern Anal. Machine Intell., vol. PAMI-5, pp. 267-287, May 1983.
[40] G. A. Carpenter, M. A. Cohen, S. Grossberg, T. Kohonen, E. Oja, G. Palm, J. J. Hopfield, and D. W. Tank, "Technical comments: Computing with neural networks,"Science, vol. 235, Mar, 1987.
[41] M. A. Arbib, "Brain theory and cooperative computation,"Human Neurobiol., vol. 4, pp. 201-218, 1985.
[42] S. Amari, "Competitive and cooperative aspects in dynamics of neural excitation and self-organization," inCompetition and Cooperation in Neural Nets, Amari and Arbib, Eds. New York: Springer-Verlag, 1982.
[43] R. Mohan, G. Medioni, and R. Nevatia, "Stereo error detection, correction, and evaluation,"IEEE Trans. Pattern Anal. Machine Intell., vol. 11, pp. 113-120, Feb. 1989.
[44] Y. Ohta and T. Kanade, "Stereo by intra and inter-scanline searching using dynamic programming,"IEEE Trans. Pattern Anal. Machine Intell., Mar. 1983.
[45] G. Medioni and R. Nevatia, "Segment-based stereo matching,"Comput. Graphics Image Processing, vol. 31, pp. 2-18, 1985.
[46] S. D. Cochran, "Steps towards accurate stereo correspondence," inProc. DARPA Image Understanding Workshop, Los Angeles, CA, Feb. 1987, pp. 777-791.
[47] H. S. Lim and T. O. Binford, "Stereo correspondence: A hierarchical approach," inProc. DARPA Image Understanding Workshop, Los Angeles, CA, Feb. 1987, pp. 234-241.
[48] R. Mohan and R. Nevatia, "Segmentation and description based on perceptual organization," inProc. IEEE Conf. Comput. Vision Patt. Recogn. (San Diego, CA), June 1989.

Index Terms:
3D structure extraction; computerised picture processing; computerised pattern recognition; perceptual organization; complex images; aerial images; collated features; segmentation; contour tracing; correspondence processes; shape description; feature groupings; computerised pattern recognition; computerised picture processing
Citation:
R. Mohan, R. Nevatia, "Using Perceptual Organization to Extract 3D Structures," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 11, no. 11, pp. 1121-1139, Nov. 1989, doi:10.1109/34.42852
Usage of this product signifies your acceptance of the Terms of Use.