Abstract
We address the issue of low-level segmentation for real-valued images. The proposed approach relies on the formulation of the problem in terms of an energy partition of the image domain. In this framework, an energy is defined by measuring a pseudo-metric distance to a source point. Thus, the choice of an energy and a set of sources determines a tessellation of the domain. Each energy acts on the image at a different level of analysis; through the study of two types of energies, two stages of the segmentation process are addressed. The first energy considered, the path variation, belongs to the class of energies determined by minimal paths. Its application as a pre-segmentation method is proposed. In the second part, where the energy is induced by a ultrametric, the construction of hierarchical representations of the image is discussed.
Similar content being viewed by others
References
P.A. Arbeláez and L.D. Cohen, “The extrema edges,” in Proc. Scale-Space'03.Skye, UK, 2003, pp. 180–195.
P.A. Arbeláez and L.D. Cohen, “Path variation and image segmentation,” in Proc. EMMCVPR'03, Lisbon, Protugal, 2003, pp. 246–260.
F. Aurenhammer and R. Klein, Handbook of Computational Geometry, Chapt. 5: Voronoi Diagrams, Elsevier Science Publishing, 2000, pp. 201–290.
J. Beaulieu and M. Goldberg, “Hierarchy in picture segmen-tation: A stepwise optimization approach,” IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 11, No. 2, pp. 150–163, 1989.
J.P. Benzécri, L'Analyse des Données. Tome I: La Taxinomie, 4th edition, Dunod: Paris, 1984.
S. Beucher and F. Meyer, Mathematical Morphology in Image Processing, Chapt. 12: The Morphological Approach to Segmentation: The Watershed Transformation, Marcel Dekker, 1992, pp. 433–481.
C.R. Brice and C.L. Fenema, “Scene analysis using regions,” Artificial Intelligence, Vol. 1, pp. 205–226, 1970.
L.D. Cohen and R. Kimmel, “Global minimum for active contour models: A minimal path approach,” International Journal of Computer Vision, Vol. 24, No. 1, pp. 57–78, 1997.
L.D. Cohen, L. Vinet, P. Sander, and A. Gagalowicz, “Hierar-chical region based stereo matching,” in Proc. IEEE Conference on Computer Vision and Pattern Recognition, 1989.
L.D. Cohen, “Multiple contour finding and perceptual grouping using minimal paths,” Journal of Mathematical Imaging and Vision, Vol. 14, No. 3, pp. 225–236, 2001.
T. Deschamps and L.D. Cohen, “Fast extraction of minimal paths in 3D images and applications to virtual endoscopy,” Medical Image Analysis, Vol. 5, No. 4, pp. 281–299, 2001.
E.W. Dijkstra, “A Note on two problems in connection with graphs,” Numerische Mathemetic, Vol. 1, pp. 269–271, 1959.
L. Garrido, P. Salembier, and D. Garcia, “Extensive operators in partition lattices for image sequence analysis,” Signal Process-ing, Vol. 66, No. 2, pp. 157–180, 1998. Special Issue on Video Sequence Segmentation.
M. Grimaud, “New measure of contrast: Dynamics,” in Image Algebra and Morphological Processing III, San Diego, USA, 1992.
M. Gromov, Metric Structures for Riemannian and Non-Riemannian Spaces, Birkhauser: Boston, 1999.
E. Hewitt and K. Stromberg, Real and Abstract Analysis, Springer Verlag, 1969.
S.L. Horowitz and T. Pavlidis, “Picture segmentation by a directed split-and-merge procedure,” in Proceedings of the Second International Joint Conference on Pattern Recognition, 1974, pp. 424–433.
C. Jordan, “Sur la Série de Fourier,” Comptes Rendus de l'Académie des Sciences. Série Mathématique., Vol. 92, No. 5, pp. 228–230, 1881.
J. L. Kelley, General Topology, Springer, 1975.
R. Kimmel and A.M. Bruckstein, “Global shape from shad-ing,” Computer Vision and Image Understanding, Vol. 62, No. 3, pp. 360–369, 1995.
R. Kimmel, N. Kiryati, and A.M. Bruckstein, “Distance maps and weighted distance transforms,” Journal of Mathematical Imaging and Vision, Vol. 6, pp. 223–233, 1996. Special Issue on Topology and Geometry in Computer Vision.
G. Koepfler, C. Lopez, and J.M. Morel, “A multiscale algorithm for image segmentation by variational method,” SIAM Journal on Numerical Analysis, Vol. 31, No. 1, pp. 282–299, 1994.
A.S. Kronrod, “On Functions of two variables,” Uspehi Mathematical Sciences, Vol. 5, No. 35, (In Russian), 1950.
R. Kruse and A. Ryba, Data Structures and Program Design in C ++. Prentice Hall: New York, 1999.
R. Malladi and J. Sethian, “A unified approach to noise removal, image-enhancement, and shape recovery,” IEEE Transactions on Image Processing, Vol. 5, No. 11, pp. 1554–1568, 1996.
P. Maragos and M.A. Butt, “Curve evolution, differential morphology and distance transforms applied to multiscale and eikonal problems,” Fundamenta Informaticae, Vol. 41, pp. 91–129, 2000.
D. Marr, Vision, Freeman: San Francisco, 1982.
D. Martin, C. Fowlkes, D. Tal, and J. Malik, “A database of hu-man segmented natural images and its application to evaluating segmentation algorithms and measuring ecological statistics,” in: Proc. ICCV'01, Vol. II. Vancouver, Canada, 2001, pp. 416–423.
F. Meyer, A. Oliveras, P. Salembier, and C. Vachier, “Morphological tools for segmentation: Connected filters and watersheds,” Annals of Telecommunications, Vol. 52, No. 7/8, pp. 367–379, 1997.
F. Meyer, “Morphological segmentation on a neighborhood graph,” Acta Stereologica, Vol. 16, No. 3, pp. 175–182, 1997.
F. Meyer, “Hierarchies of partitions and morphological segmentation,” in: Scale Space and Morphology in Computer Vision, M. Kerckhove (ed.), 2001, pp. 161–182.
A. Montanvert, P. Meer, and A. Rosenfeld, “Hierarchical im-age analysis using irregular tessellations,” IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 13, No. 4, pp. 307–316, 1991.
P. Nacken, “Image segmentation by connectivity preserving re-linking in hierarchical graph structures,” PR, Vol. 28, No. 6, pp. 907–920, 1995.
I.P. Natansson, Theory of Functions of a Real Variable.New York: Frederick Ungar Publishing, 1964.
A. Okabe, B. Boots, K. Sugihara, and S.N. Chiu, Spatial Tesselations: Concepts and Applications of Voronoi Diagrams, 2nd edition, Wiley, 2002.
S. Osher and L. Rudin, “Feature-oriented image enhancement using shock filters,” NumAnal, Vol. 27, No. 4, pp. 919–940, 1990.
L. Rudin, S. Osher, and E. Fatemi, “Nonlinear total variation based noise removal algorithms,” Physica D, Vol. 60, pp. 259–268, 1992.
J. Serra and P. Salembier, “Connected operators and pyramids,” in: Image Algebra and Mathematical Morphology, SPIE (Ed.), Vol. 2030. San Diego CA., 1993, pp. 65–76.
J.A. Sethian, Level Set Methods and Fast Marching Methods, 2nd edition Cambridge University Press: Cambridge, UK, 1999.
C. Vachier, “Extraction de caractéristiques, segmentation d'Image et morphologie mathématique,” Ph.D. thesis, Ecole des Mines de Paris, 1995.
T. Vlachos and A.G. Constantinides, “Graph-theoretical approach to colour picture segmentation and contour classification,” in: IEE Proc. Vision, Image and Sig. Proc., Vol. 140, pp. 36–45, 1993.
W. Yu, J. Fritts, and F. Sun, “A hierarchical image segmentation algorithm,” in: Proc. ICME'02, 2002, pp. 221–224.
Author information
Authors and Affiliations
Rights and permissions
About this article
Cite this article
Arbeláez, P.A., Cohen, L.D. Energy Partitions and Image Segmentation. Journal of Mathematical Imaging and Vision 20, 43–57 (2004). https://doi.org/10.1023/B:JMIV.0000011318.77653.44
Issue Date:
DOI: https://doi.org/10.1023/B:JMIV.0000011318.77653.44