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Neurocomputing in Space

  • Bipin Kumar Tripathi
Chapter
Part of the Studies in Computational Intelligence book series (SCI, volume 571)

Abstract

The high-dimensional neural network is becoming very popular in almost every intelligence system design, just to name few, computer vision, robotics, biometric identification, control, communication system, and forecasting are the scientific and engineering fields that take advantage of artificial neural networks (ANN) to emulate intelligent behavior. In computer vision the interpretation of 3D motion, 3D transformations, and 3D face or object recognition are important tasks. There have been many methodologies to solve them, but these methods are time consuming and weak to noise. The advantage of using neural networks for object recognition is the feasibility of a training system to capture the complex class conditional density of patterns. It will be desirable to explore the capabilities of ANN that can directly process three-dimensional information. This article discusses the machine learning from the view points of 3D vector-valued neural network and corresponding applications. The learning and generalization capacity of high-dimensional ANN is confirmed through diverse simulation examples.

Keywords

Artificial Neural Network Point Cloud Face Recognition High Dimensional Mapping Class Conditional Density 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer India 2015

Authors and Affiliations

  1. 1.Computer Science and EngineeringHarcourt Butler Technological InstituteKanpurIndia

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