Abstract
Identification of closed boundary contours is an important problem in image analysis because boundaries delineate the structural components, or objects, present in a scene. Most filter-based edge-detection methods do not have a mechanism to identify a group of edge sites that defines a complete closed object boundary. In this paper, we construct a suitable parameter space of one-pixel-wide closed boundaries for gray-scale images that reduces the complexity of the boundary identification problem. An algorithm based on stochastic processes and Bayesian methods is presented to identify an optimal boundary from this space. By defining a prior probability model and appropriately specifying transition probability functions on the space, a Markov chain Monte Carlo algorithm is constructed that theoretically converges to a statistically optimal closed boundary estimate. Moreover, this approach ensures that implementation via computer will result in a final boundary estimate that has the necessary property of closure which previous stochastic approaches have been unable to achieve.
Similar content being viewed by others
References
J.D. Banfield and A.E. Raftery, “Ice floe identification in satellite images using mathematical morphology and clustering about principle curves,”Journal of the American, Statistical Association, 87:7–16, 1992.
A.A. Barker, “Monte Carlo calculations of the radial distribution functions for a proton-electron plasma,”Australian Journal of Physics, 18:119–133, 1965.
J. Besag, “On the statistical analysis of dirty pictures,”Journal of the Royal Statistical Society: Series B, 48:259–279, 1986.
S. Castan, J. Zhao, and J. Shen, “New edge detection methods based on exponential filter,” inTenth International Conference on Pattern Recognition, Conference B: Pattern Recognition Systems and Applications, pp. 709–711, IEEE Computer Society Press, Los Alamitos, CA, 1990.
R. Cristi, “Markov and recursive least squares methods for the estimation of data with discontinuities,”IEEE Transactions on Acoustics, Speech, and Signal Processing, 38:1972–1980, 1990.
G.R. Dattatreya and L.N. Kanal, “Detection and smoothing of edge contours in images by one-dimensional Kalman techniques,”IEEE Transactions on Systems, Man, and Cybernetics, 20:159–165, 1990.
H. Elliot, H. Derin, R. Cristi, and D. Geman, “Application of the Gibbs Distribution to image segmentation,” in E.J. Wegman and D.J. DePriest (Eds.),Statistical Image Processing and Graphics:3–24, Marcel Dekker, New York, 1986.
K.B. Eom and R.L. Kashyap, “Composite edge detection with random field models,”IEEE Transactions on Systems, Man, and Cybernetics, 20:81–93, 1990.
D. Geiger and F. Girosi, “Parallel and deterministic algorithms from MRF's: Surface reconstruction,”IEEE Transactions on Pattern Analysis and Machine Intelligence, 13:401–412, 1991.
D. Geman, S. Geman, C. Graffigne, and P. Dong, “Boundary detection by constrained optimization,”IEEE Transactions on Pattern Analysis and Machine Intelligence, 12:609–628, 1990.
S. Geman and D. Geman, “Stochastic relaxation, Gibbs distributions, and the Bayesian restoration of images,”IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. PAMI-6:721–741, 1984.
R.H. Glendinning, “An evaluation of the ICM algorithm for image reconstruction,”Journal of Statistical Computation and Simulation, 31:169–185, 1989.
R.C. Gonzalez and R.E. Woods,Digital Image Processing, Addision-Wesley, New York, 1992.
J.K. Goutsias, “A theoretical analysis of Monte Carlo algorithms for the simulation of Gibbs random field images,”IEEE Transactions on Information Theory, 37:1618–1628, 1991.
P.J. Green and D.M. Titterington, “Recursive methods in image processing,”Bulletin of the International Statistical Institute, 52, Book 4:51–67, 1987.
J.M. Hammersley and P. Clifford, “Markov fields on finite graphs and lattices,” Unpublished manuscript, Oxford University, 1971.
W.K. Hastings, “Monte Carlo sampling methods using Markov Chains and their applications,”Biometrika, 57:97–109, 1970.
J.D. Helterbrand, N.A.C. Cressie, and J.L. Davidson, “A statistical approach to identifying closed object boundaries in images,”Advances in Applied Probability, 26, 831–854, 1994.
J.D. Helterbrand,Spatial Dependence Models and Image Analysis, Ph.D. Thesis, Iowa State University, 1993.
T.H. Hong and A. Rosenfeld, “Compact region extraction using weighted pixel linking in a pyramid,”IEEE Transactions on Pattern Analysis and Machine Intelligence, PAMI-6:222–229, 1984.
A. Huertas and G. Medioni, “Detection of intensity changes with subpixel accuracy using Laplacian-Gaussian masks,”IEEE Transactions on Pattern Analysis and Machine Intelligence, PAMI-8:651–664, 1986.
H. Jeong and C.I. Kim, “Adaptive determination of filter scales for edge detection,”IEEE Transactions on Pattern Analysis and Machine Intelligence, 14:579–585, 1992.
R. Kindermann and J.L. Snell,Markov Random Fields and Their Applications, Vol. 1, American Mathematical Society, Providence, RI, 1980.
A. Kundu, “Robust edge detection,”Pattern Recognition, 23(5):423–440, 1990.
Y. Lu and R.C. Jain, “Reasoning about edges in scale space,”IEEE Transactions on Pattern Analysis and Machine Intelligence, 14:450–468, 1992.
B.S. Manjunath and R. Chellappa, “Unsupervised texture segmentation using Markov random field models,”IEEE Transactions on Pattern Analysis and Machine Intelligence, 13:478–482, 1991.
R. Mehrotra and S. Nichani, “Corner detection,”Pattern Recognition, 23(11):1223–1233, 1990.
N. Metropolis, A.W. Rosenbluth, M.N. Rosenbluth, and A.H. Teller, “Equation of state calculations by fast computing machines,”The Journal of Chemical Physics, 21:1087–1092, 1953.
R. Park and P. Meer, “Edge-preserving artifact-free smoothing with image pyramids,”Pattern Recognition Letters, 12:467–475, 1991.
I. Pitas, “Markovian image models for image labeling and edge detection,”Signal Processing, 15:365–374, 1988.
G.X. Ritter, “Recent developments in image algebra,”Advances in Electronics and Electronic Physics, 80:243–308, 1991.
G.X. Ritter, J.N. Wilson, and J.L. Davidson, “Image algebra: An overview,”Computer Vision, Graphics, and Image Processing, 49:297–331, 1990.
S. Sarkar and K.L. Boyer, “Optimal, efficient, recursive edge detection filters,” inTenth International Conference on Pattern Recognition, Conference B: Pattern Recognition Systems and Applications: pp. 931–936, IEEE Computer Society Press, Los Alamitos, CA, 1990.
R.J. Schalkoff,Digital Image Processing and Computer Vision, Wiley, New York, 1989.
T.H. Short, “An algorithm for the detection and measurement of rail surface defects,”Journal of the American Statistical Association, 88:436–440, 1993.
J.S. Shu, “One-pixel-wide edge detection,”Pattern Recognition, 22:665–673, 1989.
M.M. Trivedi and C.X. Chen, “Object detection by step-wise analysis of spectral, spatial, and topographic features,”Computer Vision, Graphics, and Image Processing, 51:235–255, 1990.
Author information
Authors and Affiliations
Rights and permissions
About this article
Cite this article
Helterbrand, J.D., Davidson, J.L. & Cressie, N. Optimal closed boundary identification in gray-scale imagery. J Math Imaging Vis 5, 179–205 (1995). https://doi.org/10.1007/BF01248371
Issue Date:
DOI: https://doi.org/10.1007/BF01248371