Fundamentals of digital image processing
Fundamentals of digital image processing
The image processing handbook (2nd ed.)
The image processing handbook (2nd ed.)
The robust estimation of multiple motions: parametric and piecewise-smooth flow fields
Computer Vision and Image Understanding
Digital Picture Processing
Measuring Shape: Ellipticity, Rectangularity, and Triangularity
ICPR '00 Proceedings of the International Conference on Pattern Recognition - Volume 1
Robust principal component analysis by self-organizing rules based on statistical physics approach
IEEE Transactions on Neural Networks
Robust normalization of silhouettes for recognition applications
Pattern Recognition Letters - Special issue: Discrete geometry for computer imagery (DGCI'2002)
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The normalization of a binary shape is a necessary step in many image processing tasks based on image domain operations. When one must deal with deformable shapes (due to the projection of non-rigid objects onto the image plane or small changes in the position of the view point), the traditional approaches doesn't perform well. This paper presents a new method for shape normalization based on robust statistics techniques, which allows to keep the location and orientation of shapes constant independent of the possible deformations they can suffer. A numerical comparison of the sensitivity of both methods is used as a measure to validate the proposed technique, together with a ratio of areas between the non-overlapping regions and the overlapping regions of the normalized shapes. The results presented, involving synthetic and real shapes, show that the new normalization approach is much more reliable and robust that the traditional one.