On the Detection of the Axes of Symmetry of Symmetric and Almost Symmetric Planar Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Two methods for detecting symmetries
Pattern Recognition Letters
Context-free attentional operators: the generalized symmetry transform
International Journal of Computer Vision - Special issue on qualitative vision
Comments on "Symmetry as a Continuous Feature"
IEEE Transactions on Pattern Analysis and Machine Intelligence
Massively parallel data flow computer dedicated to real-time image processing
Integrated Computer-Aided Engineering
Division-Based Analysis of Symmetry and Its Application
IEEE Transactions on Pattern Analysis and Machine Intelligence
Local operators to detect regions of interest
Pattern Recognition Letters - special issue on pattern recognition in practice V
Similarity and Symmetry Measures for Convex Shapes Using Minkowski Addition
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
IEEE Transactions on Pattern Analysis and Machine Intelligence
Symmetry as a Continuous Feature
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
Shape symmetry analysis of breast tumors on ultrasound images
Computers in Biology and Medicine
Self-similarity and points of interest in textured images
PerMIn'12 Proceedings of the First Indo-Japan conference on Perception and Machine Intelligence
Hi-index | 0.01 |
In this paper we introduce a new symmetry feature named ''symmetry kernel'' (SK) to support a measure of symmetry. Given any symmetry transform S, SK of a pattern P is the maximal included symmetric sub-set of P for all directions and shifts. We provide a first algorithm to exhibit this kernel where the centre of symmetry is assumed to be the centre of mass. Then we prove that, in any direction, the optimal axis corresponds to the maximal correlation of a pattern with its symmetric version. That leads to a second algorithm. The associated symmetry measure is a modified difference between the respective surfaces of a pattern and its kernel. A series of experiments supports the actual algorithm validation.