A comparison of measures for detecting natural shapes in cluttered backgrounds

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 1407, Issue , 1998, Pages 432-448

  Williams, Lance R ^a   Thornber, Karvel K ^b  

^a University of NewMexico   (United States)

^b NEC LABORATORIES AMERICA   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

PROBABILITY DISTRIBUTIONS; RANDOM PROCESSES; STOCHASTIC SYSTEMS;

AFFINITY VALUES; CLUTTERED BACKGROUNDS; FALSE POSITIVE RATES; GESTALT PRINCIPLES; PERFORMANCE CRITERION; SALIENCY MEASURE; STOCHASTIC MOTION; TRANSITION PROBABILITY MATRIX;

COMPUTER VISION;

EID: 84957807569     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/BFb0054757     Document Type: Conference Paper

References (17)

1. Alter, T. and R. Basri, Extracting Salient Contours from Images: An Analysis of the Saliency Network, Proc. IEEE Conf. on Comp. Vision and Pattern Recognition (CVPR '96), pp. 13-20, San Francisco, CA, 1996.
2. Grossberg, S., and E. Mingolla, Neural Dynamics of Form Perception: Boundary Completion, Illusory Figures, and Neon Color Spreading, Psychological Review 92, pp. 173-211, 1985.
3. Guy, G. and G. Medioni, Inferring Global Perceptual Contours from Local Features, I ntl. Journal of Computer Vision 20, pp. 113-133, 1996.
4. Horn, B.K.P., The Curve of Least Energy, MIT AI Lab Memo No. 612, MIT, Cambridge, Mass.,1981.
5. Hdrault, L. and R. Horaud, Figure-Ground Discrimination: A Combinatorial Optimization Approach, IEEE Trans. on Pattern Analysis and Machine Intelligence 15, pp. 899-914, 1993.
6. Montanari, U., On the Optimal Detection of Curves in Noisy Pictures, Comm. of the Assoc. for Computing Machinery 14, pp. 335-345, 1971.
7. Mumford, D., Elastica and Computer Vision, Algebraic Geometry and Its Applications, Chandrajit Bajaj (ed.), Springer-Verlag, New York, 1994.
8. Press, W.H., Flannery, B.P., Teukolsky, S.A., and W.T. Vetterling, Numerical Recipes in C, Cambridge University Press, 1988.
9. Rosenfeld, A., Hummel R., and S. Zucker, Scene Labeling by Relaxation Operations, IEEE Trans. on Systems Man and Cybernetics 6, pp. 420-433, 1976.
10. Sarkar, S. and K. Boyer, Quantitative Measures of Change based on Feature Organization: Eigenvalues and Eigenvectors, Proc. IEEE Conf. Computer Vision and Pattern Recognition (CVPR '96), pp. 478-483, San Francisco, CA, 1996.
11. Shashua, A. and S. Ullman, Structural Saliency: The Detection of Globally Salient Structures Using a Locally Connected Network, 2nd Intl. Conf. on Computer Vision, Clearwater, FL, 1988.
12. Sharon, E., Brandt, A., and R. Basri, Completion Energies and Scale, Proc IEEE Conf. Computer Vision and Pattern Recognition (CVPR '97), pp. 884-890, San Juan, Puerto Rico, 1997.
13. Thornber, K.K. and L.R. Williams, Analytic Solution of Stochastic Completion Fields, Biological Cybernetics T5, pp. 141-151, 1996.
14. Ullman, S., Filling-in the Gaps: The Shape of Subjective Contours and a Model for Their Generation, Biological Cybernetics 21, pp. 1-6, 1976.
15. Williams, L.R., Wang, T. and K.K. Thornber, Computing Stochastic Completion Fields in Linear-Time Using a Resolution Pyramid, Proc. of 7th Intl. Conf. on Computer Analysis of Images and Patterns (CAIP '97), Kiel, Germany, 1997.
16. Williams, L.R. and D.W. Jacobs, Stochastic Completion Fields: A Neural Model of Illusory Contour Shape and Salience, Neural Computation 9, pp. 849-870, 1997.
17. Yen, S. and L. Finkel, "Pop-Out" of Salient Contours in a Network Based on Striate Cortical Connectivity, Investigative Opthalmology and Visual Science (ARVO), Vol. 37, No. 3, 1996.