Shape Preserving Digitization of Ideal and Blurred Binary Images
Part of the
Lecture Notes in Computer Science
book series (LNCS, volume 2886)
In order to make image analysis methods more reliable it is important to analyse to what extend shape information is preserved during image digitization. Most existing approaches to this problem consider topology preservation and are restricted to ideal binary images. We extend these results in two ways. First, we characterize the set of binary images which can be correctly digitized by both regular and irregular sampling grids, such that not only topology is preserved but also the Hausdorff distance between the original image and the reconstruction is bounded. Second, we prove an analogous theorem for gray scale images that arise from blurring of binary images with a certain filter type. These results are steps towards a theory of shape digitization applicable to real optical systems.
KeywordsSampling Point Binary Image Point Spread Function Adjacent Pixel Sampling Theorem
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Latecki, L.J., Conrad, C., Gross, A.: Preserving Topology by a Digitization Process. Journal of Mathematical Imaging and Vision 8, 131–159 (1998)MATHCrossRefMathSciNetGoogle Scholar
Latecki, L.J.: Discrete Representation of Spatial Objects in Computer Vision. Kluwer Academic Publishers, Dordrecht (1998)Google Scholar
Pavlidis, T.: Algorithms for Graphics and Image Processing. Computer Science Press, Rockville (1982)Google Scholar
Ronse, C., Tajine, M.: Discretization in Hausdorff Space. Journal of Mathematical Imaging and Vision 12, 219–242 (2000)MATHCrossRefMathSciNetGoogle Scholar
Serra, J.: Image Analysis and Mathematical Morphology. Academic Press, New York (1982)MATHGoogle Scholar
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