Surface approximation using growing self-organizing nets and gradient information

被引：5

作者：

Department of Electrical Engineering and Computer Sciences, CINVESTAV del IPN, Unidad Guadalajara, Av. Científica 1145, El Bajío, Zapopan, Jalisco, 45010, Mexico ^{[1
]}

机构：

[1] Department of Electrical Engineering and Computer Sciences, CINVESTAV del IPN, Unidad Guadalajara, El Bajío, Zapopan, Jalisco, 45010

来源：

Appl. Bionics Biomech. | 2007年 / 3卷 / 125-136期

关键词：

2D and 3D reconstruction; Geometric algebra; Gradient vector flow; Segmentation; Self-organizing neural networks;

D O I：

10.1080/11762320701797745

中图分类号：

学科分类号：

摘要：

In this paper we show how to improve the performance of two self-organizing neural networks used to approximate the shape of a 2D or 3D object by incorporating gradient information in the adaptation stage. The methods are based on the growing versions of the Kohonen's map and the neural gas network. Also, we show that in the adaptation stage the network utilizes efficient transformations, expressed as versors in the conformal geometric algebra framework, which build the shape of the object independent of its position in space (coordinate free). Our algorithms were tested with several images, including medical images (CT and MR images). We include also some examples for the case of 3D surface estimation. © 2007 Taylor & Francis.

引用

页码：125 / 136

页数：11

共 11 条

[1]

Andrade M.C., An interactive algorithm for image smoothing and segmentation, Electron Lett Comput Vis Image Anal, 4, 1, pp. 32-48, (2004)

[2]

Angelopoulou A., Psarrou A., Garcia Rodriguez J., Revett K., Automatic landmarking of 2D medical shapes using the growing neural gas network, Proceedings of the International Conference on Computer Vision, ICCV, pp. 210-219, (2005)

[3]

Bayro-Corrochano E., Robot perception and action using conformal geometry, Handbook of Geometric Computing. Applications in Pattern Recognition, Computer Vision, Neuro computing and Robotics, pp. 405-458, (2005)

[4]

Fritzke B., A growing neural gas network learns topologies, Advances in Neural Information Processing Systems, (1995)

[5]

Mehrotra K., Mohan C., Ranka S., Unsupervised learning, Elements of Artificial Neural Networks, pp. 157-213, (1997)

[6]

Moon N., Bullit E., van Leemput K., Gerig G., Model based brain and tumor segmentation, Proceedings of the 16th International Conference on Pattern Recognition, pp. 528-531, (2002)

[7]

Moon N., Bullit E., van Leemput K., Gerig G., Automatic brain and tumor segmentation, Proceedings of the Fifth International Conference on Medical Image Computing and Computer Assisted Intervention, pp. 372-379, (2002)

[8]

Prastawa M., Bullit E., Gerig G., Robust estimation for brain tumor segmentation, Conference on Medical Image Computing and Computer Assisted Intervention, 2, pp. 530-537, (2003)

[9]

Perwass C., Hildenbrand D., Aspects of geometric algebra in Euclidean, projective and conformal space. Christian-Albrechts-University of Kiel, (2003)

[10]

Rosenhahn B., Sommer G., Pose estimation in conformal geometric algebra. Christian-Albrechts-University of Kiel, pp. 13-36, (2002)

← 1 2 →