Medial Axis Extraction Using Growing Neural Gas

Zhang, Yee; Liu, Guisong; Fang, Xiufen; Chen, Bin

doi:10.1109/aici.2009.47

Cited by 1 publication

(1 citation statement)

References 14 publications

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“…There are very few cases of the application in shape representation of SOMs in general, such as [18], [19], and the GNG in particular, such as [6], [4], [24], [3], [2], [1]. To the best of our knowledge, the GNG has previously never been applied explicitly to the task of shape curvature analysis.…”

Section: Introductionmentioning

confidence: 99%

Region analysis through close contour transformation using growing neural gas

Gupta

Psarrou

Angelopoulou

et al. 2012

The 2012 International Joint Conference on Neural Networks (IJCNN)

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In our work we aim to explore a general framework that addresses the fundamental problem of universal unsupervised extraction of semantically meaningful visual regions. To this end this paper describes a novel region analysis technique using a self-organising map, the growing neural gas, which is adapted so as to improve modelling speed as well as to ensure a double-linkage chain around all region contours to simplify shape analysis. While the growing neural gas has been extensively applied to shape modelling, it has never explicitly been used for curvature analysis, contour description and region similarity. Once a contour network has been obtained, a transformation is applied that converts the closed contour to an open one, facilitating the use of certain angular descriptors. Discriminative descriptors derived from the properties of regions, their contours and their transformed contours are established and define a feature vector used for the representation of regions based on the appearance and contour information.

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