Recovering Affine Deformations of Fuzzy Shapes
SCIA '09 Proceedings of the 16th Scandinavian Conference on Image Analysis
Pixel Coverage Segmentation for Improved Feature Estimation
ICIAP '09 Proceedings of the 15th International Conference on Image Analysis and Processing
Sub-pixel Segmentation with the Image Foresting Transform
IWCIA '09 Proceedings of the 13th International Workshop on Combinatorial Image Analysis
Anti-aliased Euclidean distance transform
Pattern Recognition Letters
Defuzzification of spatial fuzzy sets by feature distance minimization
Image and Vision Computing
Measuring Squareness and Orientation of Shapes
Journal of Mathematical Imaging and Vision
A graph-based framework for sub-pixel image segmentation
Theoretical Computer Science
Orientation and anisotropy of multi-component shapes from boundary information
Pattern Recognition
Distance measures between digital fuzzy objects and their applicability in image processing
IWCIA'11 Proceedings of the 14th international conference on Combinatorial image analysis
Image foresting transform: on-the-fly computation of segmentation boundaries
SCIA'11 Proceedings of the 17th Scandinavian conference on Image analysis
Measuring linearity of open planar curve segments
Image and Vision Computing
Coverage segmentation based on linear unmixing and minimization of perimeter and boundary thickness
Pattern Recognition Letters
Border extrapolation using fractal attributes in remote sensing images
Computers & Geosciences
Geometric properties of interval type-II fuzzy regions
Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology
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We present a novel method that provides an accurate and precise estimate of the length of the boundary (perimeter) of an object by taking into account gray levels on the boundary of the digitization of the same object. Assuming a model where pixel intensity is proportional to the coverage of a pixel, we show that the presented method provides error-free measurements of the length of straight boundary segments in the case of nonquantized pixel values. For a more realistic situation, where pixel values are quantized, we derive optimal estimates that minimize the maximal estimation error. We show that the estimate converges toward a correct value as the number of gray levels tends toward infinity. The method is easy to implement; we provide the complete pseudocode. Since the method utilizes only a small neighborhood, it is very easy to parallelize. We evaluate the estimator on a set of concave and convex shapes with known perimeters, digitized at increasing resolution. In addition, we provide an example of applicability of the method on real images, by suggesting appropriate preprocessing steps and presenting results of a comparison of the suggested method with other local approaches.