On Image Analysis by the Methods of Moments
IEEE Transactions on Pattern Analysis and Machine Intelligence
Finding local maxima in a pseudo-Euclidean distance transform
Computer Vision, Graphics, and Image Processing
Robust Clustering with Applications in Computer Vision
IEEE Transactions on Pattern Analysis and Machine Intelligence
Thinning Methodologies-A Comprehensive Survey
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Robust Line Fitting in a Noisy Image by the Method of Moments
IEEE Transactions on Pattern Analysis and Machine Intelligence
Hierarchical Decomposition of Multiscale Skeletons
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Least Biased Fuzzy Clustering Method
IEEE Transactions on Pattern Analysis and Machine Intelligence
An Efficient Fully Parallel Thinning Algorithm
ICDAR '97 Proceedings of the 4th International Conference on Document Analysis and Recognition
Smoothed Local Symmetries and Their Implementation
Smoothed Local Symmetries and Their Implementation
An Improved Parallel Thinning Algorithm
ICDAR '03 Proceedings of the Seventh International Conference on Document Analysis and Recognition - Volume 2
Lapped block image analysis via the method of legendre moments
EURASIP Journal on Applied Signal Processing
Generalized Laguerre expansions of multivariate probability densities with moments
Computers & Mathematics with Applications
| Hi-index | 0.00 |
This paper presents a new concept of skeletonization which produces a graph containing all the topological information needed to derive a skeleton of noisy shapes, the proposed statistical method is based on Legendre moment theory controlled by Maximum Entropy Principle (M.E.P.). We propose a new approach for estimating the underlying probability density function (p.d.f.) of input data set. Indeed the p.d.f. is expanded in terms of Legendre polynomials by means of the Legendre moments. Then the order of the expansion is selected according to the (M.E.P.). The points corresponding to the local maxima of the selected p.d.f. will be true points of the skeleton to be extracted by the proposed algorithm. We have tested the proposed Legendre Moment Skeletonization Method (LMSM) on a variety of real and simulated noisy images, it produces excellent and visually appealing results, with comparison to some well known methods.