CSDL Home IEEE Transactions on Pattern Analysis & Machine Intelligence 2008 vol.30 Issue No.07 - July

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Issue No.07 - July (2008 vol.30)

pp: 1132-1145

ABSTRACT

This paper introduces an image decomposition and simplification method based on the constrained connectivity paradigm. According to this paradigm, two pixels are said to be connected if they comply to a series of constraints defined in terms of simple measures such as the maximum grey level differences over well-defined pixel paths and regions. The resulting connectivity relation generates a unique partition of the image definition domain. The simplification of the image is then achieved by setting each segment of the partition to the mean value of the pixels falling within this segment. Fine to coarse partition hierarchies (and therefore images of increasing degree of simplification) are produced by varying the threshold value associated with each connectivity constraint. The paper also includes a generalisation to multichannel images, applications, a review of related image segmentation techniques, and pseudo-code for an implementation based on queue and stack data structures.

INDEX TERMS

Image Processing and Computer Vision, Hierarchical, Region growing, partitioning, Clustering, Segmentation, Graph-theoretic methods

CITATION

Pierre Soille, "Constrained Connectivity for Hierarchical Image Partitioning and Simplification",

*IEEE Transactions on Pattern Analysis & Machine Intelligence*, vol.30, no. 7, pp. 1132-1145, July 2008, doi:10.1109/TPAMI.2007.70817REFERENCES

- [1] S. Horowitz, and T. Pavlidis, “Picture Segmentation by a Directed Split-and-Merge Procedure,”
Proc. Second Int'l Joint Conf. Pattern Recognition, pp. 424-433, 1974.- [6] J. Serra and P. Salembier, “Connected Operators and Pyramids,”
Proc. SPIE Image Algebra and Morphological Image Processing IV, vol. 2030, E. Dougherty, P. Gader, and J. Serra, eds., pp. 65-76, July 1993.- [9] F. Meyer and P. Maragos, “Morphological Scale-Space Representation with Levelings,”
Lecture Notes in Computer Science, vol. 1682, pp. 187-198, Sept. 1999.- [13] R. Duda, P. Hart, and D. Stork,
Pattern Classification, second ed. Wiley Interscience, 2000.- [24] R. Cormack, “A Review of Classification (with discussion),”
J.Royal Statistical Soc. A, vol. 134, pp. 321-367, 1971.- [26] U. Braga-Neto, “Alternating Sequential Filters by Adaptive-Neighborhood Structuring Functions,”
Proc. Int'l Symp. Math. Morphology and Its Applications to Image and Signal Processing, Computational Imaging and Vision, vol. 5, pp. 139-146, Kluwer, 1996.- [28] M. Nagao and T. Matsuyama,
A Structural Analysis of Complex Aerial Photographs. Plenum, 1980.- [33] V. Nunes de Lima,
IMAGE2000 and CLC2000: Products and Methods, EUR 21757 EN. Joint Research Centre, http://www.ec-gis.org/sdi/publist/pdfsnunes2005eur2000.pdf , 2005.- [35] P. Besl and R. Jain, “Segmentation through Variable-Order Surface Fitting,”
IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 10, no. 2, pp. 167-192, Mar. 1988.- [37] R. Schoenmakers, “Integrated Methodology for Segmentation of Large Optical Satellite Images in Land Applications of Remote Sensing,” EUR 16292 EN, European Commission, Joint Research Centre, Sept. 1995, PhD dissertation of Katholieke Universiteit Nijmegen, http://www.cs.ru.nl/ths/dissertationsSchoenmakers-phd95.ps.gz .
- [49] W. Kropatsch and Y. Haxhimusa, “Grouping and Segmentation in a Hierarchy of Graphs,”
Proc. 16th IS&T SPIE Ann. Symp. Computational Imaging II, C. Bouman and E. Miller, eds., pp. 193-204, May 2004.- [56] R. Adams and L. Bischof, “Seeded Region Growing,”
IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 16, no. 6, pp. 641-647, June 1994.- [61] J.-P. Sutcliffe, “On the Logical Necessity and Priority of a Monothetic Conception of Class, and on the Consequent Inadequacy of Polythetic Accounts of Category and Categorization,”
New Approaches in Classification and Data Analysis, E. Diday, Y.Lechevallier, M. Schrader, P. Bertrand, and B. Burtchy, eds., pp.55-63, Springer, 1994. |