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Image Segmentation According to the Movement of Real Objects

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Variational and Level Set Methods in Image Segmentation

Part of the book series: Springer Topics in Signal Processing ((STSP,volume 5))

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Abstract

In the preceding chapter, image segmentation used optical flow. The flow components in each region were described by a linear parametric model. Therefore, the model parameters served to distinguish the segmentation regions. We presented two methods which differed by their representation of the segmentation boundaries. One of the methods (Chapter 7, Section 7.2) can be viewed as an optical flow edge detection method. It used the minimum description length (MDL) formulation [1], and placed the segmentation boundary edges at points between adjacent pixels described by distinct model parameters. The other method we described was a level set method, using closed curves to delineate the segmentation regions each of which is described by the parameters of a general linear model of image motion [2] In both methods, the parametric models did not express any relationship to the movement of the physical objects from which the optical flow arose.

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Authors and Affiliations

  1. Institut National de la Recherche Scientifique (INRS), 800, de la Gauchetière Ouest, Montreal, Quebec, Canada

    Prof. Amar Mitiche & Dr. Ismail Ben Ayed

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  1. Prof. Amar Mitiche
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  2. Dr. Ismail Ben Ayed
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Correspondence to Amar Mitiche .

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Mitiche, A., Ayed, I.B. (2010). Image Segmentation According to the Movement of Real Objects. In: Variational and Level Set Methods in Image Segmentation. Springer Topics in Signal Processing, vol 5. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-15352-5_8

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  • DOI: https://doi.org/10.1007/978-3-642-15352-5_8

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