Shape recovery by a generalized topology preserving SOM

Authors:
Dong Huang;Zhang Yi
Affiliations:
Computational Intelligence Laboratory, School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu 610054, PR China;Computational Intelligence Laboratory, School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu 610054, PR China
Venue:
Neurocomputing
Year:
2008

Citing 12
Cited 2

Elastically deformable models

SIGGRAPH '87 Proceedings of the 14th annual conference on Computer graphics and interactive techniques
Constraints on deformable models: recovering 3D shape and nongrid motion

Artificial Intelligence
Gradient Vector Flow: A New External Force for Snakes

CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
Geodesic active contours

ICCV '95 Proceedings of the Fifth International Conference on Computer Vision
Geodesic Active Regions for Supervised Texture Segmentation

ICCV '99 Proceedings of the International Conference on Computer Vision-Volume 2 - Volume 2
CPM: A Deformable Model for Shape Recovery and Segmentation Based on Charged Particles

IEEE Transactions on Pattern Analysis and Machine Intelligence
A Charged Geometric Model for Active Contours

ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 02
A parallel growing architecture for self-organizing maps with unsupervised learning

Neurocomputing
A charged active contour based on electrostatics

ACIVS'06 Proceedings of the 8th international conference on Advanced Concepts For Intelligent Vision Systems
Shape recovery algorithms using level sets in 2-D/3-D medical imagery: a state-of-the-art review

IEEE Transactions on Information Technology in Biomedicine
Multiple contour extraction from graylevel images using an artificial neural network

IEEE Transactions on Image Processing
ViSOM - a novel method for multivariate data projection and structure visualization

IEEE Transactions on Neural Networks

Line Image Classification by NG×SOM: Application to Handwritten Character Recognition

WSOM '09 Proceedings of the 7th International Workshop on Advances in Self-Organizing Maps
Probabilistic Self-Organizing Graphs

IWANN '09 Proceedings of the 10th International Work-Conference on Artificial Neural Networks: Part I: Bio-Inspired Systems: Computational and Ambient Intelligence

Quantified Score

Hi-index	0.01

Visualization

Abstract

This paper proposes a new deformable model, i.e., gTPSOM, for object shape recovery. Inspired by visual induced self-organizing map (ViSOM) and region-aided active contour, the proposed model is formulated as generalized chain SOM with an adaptive force field. The adaptive force field is adjusted during the evolvement of the neuron chain according to local consistency of the image edge map. With the topology preserving property inherited from the data mapping model, i.e., ViSOM, the proposed model is suitable for both the precise edge detection and the complex shape recovery with boundary strength variations. Detailed formulation and analysis of the proposed model are given. Experiments on both synthesis and real images are carried out to demonstrate the performances.