Hierarchical neural networks for learning three-dimensional objects from range images

M. Takatsuka; Ray Jarvis

doi:10.1117/1.482622

1 January 1998 Hierarchical neural networks for learning three-dimensional objects from range images

M. Takatsuka, Ray Jarvis

Journal of Electronic Imaging, Vol. 7, Issue 1, (January 1998). https://doi.org/10.1117/1.482622

A free-form three-dimensional (3-D) object recognition system using artificial neural networks (ANNs) is described. The system is able to learn and recognize 3-D objects that have various surface shapes. The types of surface shapes the system is able to handle include not only predefined surfaces such as simple piecewise quadric surfaces but also more complex free-form surfaces. The system utilizes ANNs to derive induced representations and inductive learning of 3-D object classes. Starting with range image processing, the surfaces of objects are segmented into surface parts by analyzing local shape features called surface/sphere intersection signatures (SSISs). Two layers of self-organizing feature maps (SOFMs) are then used to learn those segmented surface parts and their geometrical relationships. By finding corresponding neurons in the SOFMs for all pairs of surface parts appearing in the observed object, the object is described by a binary image that represents firing states of neurons. The learning vector quantization (LVQ) network is used for learning and recognizing 3-D objects from objects' binary image descriptions. The recognition performance of the system is demonstrated using several objects.

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M. Takatsuka and Ray Jarvis "Hierarchical neural networks for learning three-dimensional objects from range images," Journal of Electronic Imaging 7(1), (1 January 1998). https://doi.org/10.1117/1.482622

Published: 1 January 1998

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