A new characterization of digital lines by least square fits
Pattern Recognition Letters
A parametrization of digital planes by least-squares fits and generalizations
Graphical Models and Image Processing
Digital approximation of moments of convex regions
Graphical Models and Image Processing
Rectilinearity Measurements for Polygons
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Comparative Evaluation of Length Estimators of Digital Curves
IEEE Transactions on Pattern Analysis and Machine Intelligence
A New Convexity Measure Based on a Probabilistic Interpretation of Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
Integral Invariants for Shape Matching
IEEE Transactions on Pattern Analysis and Machine Intelligence
On Shape of Plane Elastic Curves
International Journal of Computer Vision
Pattern Recognition
A two-component rectilinearity measure
Computer Vision and Image Understanding
Measuring Elongation from Shape Boundary
Journal of Mathematical Imaging and Vision
Measuring linearity of planar point sets
Pattern Recognition
A Unified Curvature Definition for Regular, Polygonal, and Digital Planar Curves
International Journal of Computer Vision
High-Precision Boundary Length Estimation by Utilizing Gray-Level Information
IEEE Transactions on Pattern Analysis and Machine Intelligence
Farthest point distance: A new shape signature for Fourier descriptors
Image Communication
Shape feature extraction and description based on tensor scale
Pattern Recognition
A Hu moment invariant as a shape circularity measure
Pattern Recognition
Search strategies for shape regularized active contour
Computer Vision and Image Understanding
ADR shape descriptor - Distance between shape centroids versus shape diameter
Computer Vision and Image Understanding
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In this paper we consider the distance between the shape centroid computed from the shape interior points and the shape centroid computed from the shape boundary points. We show that the distance between those centroids is upper bounded by the quarter of the perimeter of the shape considered. The obtained upper bound is sharp and cannot be improved. Next, we introduce the shape centredness as a new shape descriptor which, informally speaking, should indicate to which degree a shape has a uniquely defined centre. By exploiting the result mentioned above, we give a formula for the computation of the shape centredness. Such a computed centredness is invariant with respect to translation, rotation and scaling transformations.