This Article 
   
 Share 
   
 Bibliographic References 
   
 Add to: 
 
Digg
Furl
Spurl
Blink
Simpy
Google
Del.icio.us
Y!MyWeb
 
 Search 
   
On the Representation of Image Structures via Scale Space Entropy Conditions
November 1999 (vol. 21 no. 11)
pp. 1199-1203

Abstract—This paper deals with a novel way for representing and computing image features encapsulated within different regions of scale-space. Employing a thermodynamical model for scale-space generation, the method derives features as those corresponding to “entropy rich” image regions where, within a given range of spatial scales, the entropy gradient remains constant. Different types of image features, defining regions of different information content, are accordingly encoded by such regions within different bands of spatial scale.

[1] L. Brillouin, Science and Information Theory. New York: Academic Press, 1962.
[2] T.M. Cover and J.A. Thomas, Elements of Information Theory. John Wiley&Sons, 1991.
[3] S.R. de Groot and P. Mazur, Non-Equilibrium Thermodynamics. Amsterdam: North-Holland, 1962.
[4] J.J. Koenderink, “The Structure of Images,” Biological Cybernetics, vol 50, pp. 363-370, 1984.
[5] T. Lindeberg, Scale-Space Theory in Computer Vision. Kluwer Academic, 1994.
[6] M.C. Mackey, “The Dynamic Origin of Increasing Entropy,” Reviews of Modern Physics, vol. 61, pp. 981-1,015, 1989.
[7] D.S. Manjunath and R. Chellappa, “A Unified Approach to Boundary Perception: Edges, Textures and Illusory Contours,” IEEE Trans. Neural Networks, vol. 4, pp. 96-107, 1993.
[8] B. McCane and T. Caelli, “Multi-Scale Adaptive Segmentation Using Edge and Region-Based Attribute,” Proc. First Int'l Conf. Knowledge-Based Intelligent Electronic Systems, L.C. Jain, ed., pp. 72-81, May 1997.
[9] N. Petkov and P. Kruiziga, “Computational Models of Visual Neurons Specialised in the Detection of Periodic and Aperiodic Oriented Visual Stimuli: Bar and Grating Cells,” Biological Cybernetics, vol. 76, no. 2, pp. 83-96, 1997.
[10] X. Ran and N. Farvardin, “A Perceptually Motivated Three-Component Image Model-Part I: Description of the Model,” IEEE Trans. Image Processing, vol. 4, no. 4, pp. 401-415, 1995.
[11] P.K. Sahoo, S. Soltani, A.K.C. Wong, and Y.C. Chen, “A Survey of Thresholding Techniques,” Computer Vision, Graphics, and Image Processing, vol. 41, pp. 233-260, 1988.
[12] S. Shah and M.D. Levine, “Visual Information Processing in Primate Cone Pathways-Part I: A Model,” IEEE Trans. Systems, Man, and Cybernetics-Part B, vol. 26, no. 2, pp. 259-274, 1996.
[13] R. von der Heydt, E. Peterhans, and M.R. Dursteler, “Grating Cells in Monkey Visual Cortex: Coding Texture,” Channels in the Visual Nervous System: Neurophysiology, Psychophysics and Models, B. Blum, ed., pp. 53-73, London: Freund, 1991.
[14] R. von der Heydt, E. Peterhans, and M.R. Dursteler, “Periodic-Pattern-Selective Cells in Monkey Visual Cortex,” J. Neuroscience, vol. 12, pp. 1,416-1,434, 1992.
[15] A.P. Witkin, “Scale Space Filtering,” Proc. Int'l Joint Conf. Artificial Intelligence, pp. 1,019-1,023, 1983.

Index Terms:
Scale space, entropy production, features encoding.
Citation:
Mario Ferraro, Giuseppe Boccignone, Terry Caelli, "On the Representation of Image Structures via Scale Space Entropy Conditions," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 21, no. 11, pp. 1199-1203, Nov. 1999, doi:10.1109/34.809112
Usage of this product signifies your acceptance of the Terms of Use.