Kernel based symmetry measure

Authors:
Bertrand Zavidovique;Vito Di Gesù
Affiliations:
DMA, Università di Palermo, Italy;IEF, University of Paris XI, ORSAY, France
Venue:
ICIAP'05 Proceedings of the 13th international conference on Image Analysis and Processing
Year:
2005

Citing 8
Cited 1

On the Detection of the Axes of Symmetry of Symmetric and Almost Symmetric Planar Images

IEEE Transactions on Pattern Analysis and Machine Intelligence
N-folded Symmetries by Complex Moments in Gabor Space and their Application to Unsupervised Texture Segmentation

IEEE Transactions on Pattern Analysis and Machine Intelligence
Division-Based Analysis of Symmetry and Its Application

IEEE Transactions on Pattern Analysis and Machine Intelligence
Detecting Symmetry in Grey Level Images: The Global Optimization Approach

International Journal of Computer Vision
Symmetry Detection by Generalized Complex (GC) Moments: A Close-Form Solution

IEEE Transactions on Pattern Analysis and Machine Intelligence
A Reflective Symmetry Descriptor

ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part II
Finding Symmetry in Intensity Images TITLE2:

Finding Symmetry in Intensity Images TITLE2:
A note on the iterative object symmetry transform

Pattern Recognition Letters

Biological shape characterization for automatic image recognition and diagnosis of protozoan parasites of the genus Eimeria

Pattern Recognition

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Abstract

In this paper we concentrate on a measure of symmetry. Given a transform S, the kernel SK of a pattern is defined as the maximal included symmetric sub-set of this pattern. A first algorithm is outlined to exhibit this kernel. The maximum being taken over all directions, the problem arises to know which center to use. Then the optimal direction triggers the shift problem too. As for the measure we propose to compute a modified difference between respective surfaces of a pattern and its kernel. A series of experiments supports actual algorithm validation.