Vitorino Ramos: On Image Filtering, Noise and Morphological Size Intensity Diagrams

25. Vitorino Ramos, Fernando Muge; On Image Filtering, Noise and Morphological Size Intensity Diagrams, RecPad´2000 - 11th Portuguese Conference on Pattern Recognition, in Aurélio C. Campilho and A.M. Mendonça (Eds.), ISBN 972-96883-2-5, pp. 483-491, Porto, Portugal, May 11-12, 2000.

Vitorino Ramos - Mathematical Morphology, Noise filtering and Size Intensity Diagrams

Figures - On the left, the original "House" noise-free image (left) and the respective Histogram for several MM openings and the Size Intensity Diagram (rigth), coded in an grey 8-bit image format. On the rigth, the same diagrams for "House" added with 50% random salt and pepper noise. Differences on this diagrams allow us to identify the proper filters for noise removal.

PDF file: paper (754 Kb)

Abstract: In the absence of a pure noise-free image it is hard to define what noise is, in any original noisy image, and as a consequence also where it is, and in what amount. In fact, the definition of noise depends largely on our own aim in the whole image analysis process, and (perhaps more important) in our self-perception of noise. For instance, when we perceive noise as disconnected and small it is normal to use MM-ASF filters to treat it. There is two evidences of this. First, in many instances there is no ideal and pure noise-free image to compare our filtering process (nothing but our self-perception of its pure image); second, and related with this first point, MM transformations that we chose are only based on our self - and perhaps - fuzzy notion. This also yields a third point: that once the appropriate filter is found, it is no longer applicable for a new noisy image, with a different kind of noise intensity, distribution and size. In other words, the design of MM filtering algorithms for one particular noise-removal problem and by using our perception is only extended for similar images. Algorithm robustness and adaptation is no longer possible. However, in the absence of that ideal pure noise-free image and by using the strategy of comparing two simultaneous filtering process on the same original noisy image, it is possible to find some relations that can help us, one step more through the direction of automatically chose the right filtering process. The present proposal combines the results of two MM filtering transformations (FT1, FT2) and makes use of some measures and quantitative relations on their Size/Intensity Diagrams to find the most appropriate noise removal process. Results can also be used for finding the most appropriate stop criteria, and the right sequence of MM operators combination on Alternating Sequential Filters (ASF), if these measures are applied, for instance, on a Genetic Algorithm’s target function.

Keywords: Mathematical Morphology, Noise characterization, Noise Filtering, Image Analysis, Image Processing, Pattern Recognition.

Cited by:

º Jarmo T. Alander, "An Indexed Bibliography of Genetic Algorithms in Optics and Image Processing", Department of Electrical Engineering and Automation, University of Vaasa, Finland, March 2006.

Related Works:

55. Vitorino Ramos, Pedro Pina, Exploiting and Evolving Rⁿ Mathematical Morphology Feature Spaces, in Ronse Ch., Najman L., Decencière E. (Eds.), Mathematical Morphology: 40 Years On, pp. 465-474, Springer, Dordrecht, The Netherlands, 2005.

29. Vitorino Ramos, Filipe Almeida, Artificial Ant Colonies in Digital Image Habitats - A Mass Behaviour Effect Study on Pattern Recognition, Proceedings of ANTS´2000 - 2nd International Workshop on Ant Algorithms (From Ant Colonies to Artificial Ants), Marco Dorigo, Martin Middendorf & Thomas Stüzle (Eds.), pp. 113-116, Brussels, Belgium, 7-9 Sep. 2000.

59. Carlos Fernandes, Vitorino Ramos and Agostinho C. Rosa, Self-Regulated Artificial Ant Colonies on Digital Image Habitats, in Int. Journal of Lateral Computing, IJLC, vol. 2, nº 1, pp. 1-8, ISSN 0973-208X, Dec. 2005.

70. Ramos, V., Fernandes, C., Rosa, A.C., Abraham, A., Computational Chemotaxis in Ants and Bacteria over Dynamic Environments, submitted to CEC´07 - Congress on Evolutionary Computation, IEEE Press, Singapore, 25-28 Sep. 2007.

69. Fernandes, C., Rosa, A.C., Ramos V., Binary Ant Algorithm, to appear in GECCO´07 - Genetic and Evolutionary Computation Conference, ACM Press, London, UK, 7-11 July, 2007.

63. Vitorino Ramos, Carlos Fernandes, Agostinho C. Rosa, Social Cognitive Maps, Swarm Collective Perception and Distributed Search on Dynamic Landscapes, submitted to A. Porto, A. Pazos, W. Buno (Eds.), Advancing Artificial Intelligence through Biological Process Applications, IDEA Group Inc., 2007.

31. Vitorino Ramos, Fernando Muge, Map Segmentation by Colour Cube Genetic K-Mean Clustering, Proc. of ECDL´2000 - 4th European Conference on Research and Advanced Technology for Digital Libraries, J. Borbinha and T. Baker (Eds.), ISBN 3-540-41023-6, Lecture Notes in Computer Science, Vol. 1923, pp. 319-323, Springer-Verlag -Heidelberg, Lisbon, Portugal, 18-20 Sep. 2000.

51. Vitorino Ramos, Ajith Abraham, Evolving a Stigmergic Self-Organized Data-Mining, in ISDA-04, 4th Int. Conf. on Intelligent Systems, Design and Applications, Budapest, Hungary, ISBN 963-7154-30-2, pp. 725-730, August 26-28, 2004.

53. Vitorino Ramos, Jonathan Campbell, John Slater, John Gillespie, Ivan F. Bendezu and Fionn Murtagh, Swarming around Shellfish Larvae Images, in WCLC-05, 2nd World Congress on Lateral Computing, Bangalore, India, 16-18 Dec., 2005.

45. Vitorino Ramos, Ajith Abraham, Swarms on Continuous Data, in CEC´03 - Congress on Evolutionary Computation, IEEE Press, ISBN 078-0378-04-0, pp.1370-1375, Canberra, Australia, 8-12 Dec. 2003.