Abstract
Texture segmentation based on local morphological pattern spectra provides an attractive alternative to linear scale spaces as the latter suffer from blurring and do not preserve the shape of image features. However, for successful segmentation, pattern spectra derived using a number of structuring elements, often at different orientations, are required. This paper addresses this problem by using area morphology to generate a single pattern spectrum, consisting of a local granulometry and anti-granulometry, at each pixel position. As only one spectrum is produced, segmentation is performed by directly using the spectrum as the feature vector instead of taking pattern spectrum moments. Segmentation results for a simulated image of Brodatz textures and test images from the Outex texture database show the potential of the new approach.
Neil Fletcher supported by an EPSRC CASE award in conjunction with QinetiQ.
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Fletcher, N.D., Evans, A.N. (2005). Texture Segmentation Using Area Morphology Local Granulometries. In: Ronse, C., Najman, L., Decencière, E. (eds) Mathematical Morphology: 40 Years On. Computational Imaging and Vision, vol 30. Springer, Dordrecht. https://doi.org/10.1007/1-4020-3443-1_33
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DOI: https://doi.org/10.1007/1-4020-3443-1_33
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