Object shape extraction based on the piecewise linear skeletal representation

Authors:
Roman M. Palenichka;Marek B. Zaremba
Affiliations:
Dept. of Computer Science and Engineering, Université du Québec en Outaouais, Gatineau, Canada;Dept. of Computer Science and Engineering, Université du Québec en Outaouais, Gatineau, Canada
Venue:
ICIAR'05 Proceedings of the Second international conference on Image Analysis and Recognition
Year:
2005

Citing 8
Cited 0

Distance transformations in digital images

Computer Vision, Graphics, and Image Processing
A survey of thresholding techniques

Computer Vision, Graphics, and Image Processing
Pattern Spectrum and Multiscale Shape Representation

IEEE Transactions on Pattern Analysis and Machine Intelligence
Detecting salient blob-like image structures and their scales with a scale-space primal sketch: a method for focus-of-attention

International Journal of Computer Vision
A Model of Saliency-Based Visual Attention for Rapid Scene Analysis

IEEE Transactions on Pattern Analysis and Machine Intelligence
Note on the multiscale representation of 2D and 3D shapes

Graphical Models and Image Processing
Learning and Design of Principal Curves

IEEE Transactions on Pattern Analysis and Machine Intelligence
Self-organizing maps for the skeletonization of sparse shapes

IEEE Transactions on Neural Networks

Quantified Score

Hi-index	0.00

Visualization

Abstract

The goal of the skeletal shape extraction algorithm presented in this paper was to obtain a concise and robust description of planar shapes for object recognition and subsequent region segmentation. The solution of this problem is proposed in the form of a piecewise-linear skeletal representation of planar shapes, which is a very economical shape description, resistant to distortions and intensity changes. A vertex growing procedure – similar to that of pixel-by-pixel region growing – have been developed to obtain rapidly piecewise linear skeletons of gray-scale object regions without their segmentation. Simultaneously, the complete planar shape of the objects of interest is extracted by a locally-adaptive binarization performed locally at the skeleton vertex areas. The vertex extraction is implemented using a visual attention operator, which can measure the saliency level of image fragments and select vertices at the local maxima of this operator.