Abstract
An original neural scheme for segmentation of range data is presented, which is part of a more general 3D vision system for robotic applications. The entire process relies on a neural architecture aimed to perform first order image irradiance analysis, that is local estimation of magnitude and orientation of the image irradiance gradient.
In the case of dense 3D data, irradiance is replaced by depth information so irradiance analysis of these pseudo-images provides knowledge about the actual curvature of the acquired surfaces. In particular, boundaries and contours due to mutual occlusions can be detected very well while there are no false contours due to rapid changing in brightness or color.
To this aim, after a noise reduction step, both magnitude and phase distributions of the gradient are analysed to perform complete contour detection, and all continuous surfaces are segmented.
Theoretical foundations of the work are reported, along with the description of the architecture and the first experimental results.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Chella, A., Gaglio, S., Pirrone, R.: Conceptual Representations of Actions for Autonomous Robots. Robotics and Autonomous Systems 34, 251–263 (2001)
Gupta, A., Bajcsy, R.: Volumetric Segmentation of Range Images of 3-D Objects Using Superquadric Models. Computer Vision, Graphics and Image Processing: Image Understanding 58, 302–326 (1993)
Ferrie, F., Lagarde, J., Whaite, P.: Darboux Frames, Snakes, and Super-Quadrics: Geometry From the Bottom Up. IEEE Trans. on Pattern Analysis and Machine Intelligence 15, 771–784 (1993)
Pirrone, R.: Part based Segmentation and Modeling of Range Data by Moving Target. Journal of Intelligent Systems 11, 217–247 (2001)
Leonardis, A., Jaklic, A., Solina, F.: Superquadrics for Segmenting and Modeling Range Data. IEEE Trans. on Pattern Analysis and Machine Intelligence 19, 1289–1295 (1997)
Callari, F., Maniscalco, U.: New Robust Approach to Image and 3-D Shape Reconstruction. In: Proc. of International Conference on Computer Vision and Pattern recognition, Jerusalem, Israel, pp. 103–107 (1994)
Callari, F., Maniscalco, U., Storniolo, P.: Hybrid Methods for Robust Irradiance Analysis and 3-D Shape Reconstruction from Images. In: Proc. of International Conference on Artificial Neural Networks, Sorrento, Italy (1994)
Grossberg, S., Mingolla, E.: Neural Dynamics of Perceptual Grouping: Textures, Boundaries and Emergent Segmentation. Perception and Psychophysics 38, 141–171 (1985)
Jain, A.: Foundamentals of Digital Images Processing. Prentice-Hall, Englewood Cliffs (1988)
Kirsh, R.: Computer Determination of the Constituent Structure of Biological Images. Comput. Biomed. Res. 4, 315–328 (1971)
Marr, D., Hildreth, E.: Theory of edge detection. Proc. Royal Society 207, 187–217 (1980)
Koenderink, J., Van Doorn, A.: Representation of Local Geometry in the Visual System. Biological Cybernetics 55, 367–385 (1987)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2003 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Chella, A., Maniscalco, U., Pirrone, R. (2003). A Neural Architecture for 3D Segmentation. In: Apolloni, B., Marinaro, M., Tagliaferri, R. (eds) Neural Nets. WIRN 2003. Lecture Notes in Computer Science, vol 2859. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-45216-4_13
Download citation
DOI: https://doi.org/10.1007/978-3-540-45216-4_13
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-20227-1
Online ISBN: 978-3-540-45216-4
eBook Packages: Springer Book Archive