Real-time trajectory analysis using stacked invariance methods

Kitts, B. (1998) Real-time trajectory analysis using stacked invariance methods. [Preprint] (Unpublished)

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Invariance methods are used widely in pattern recognition as a preprocessing stage before algorithms such as neural networks are applied to the problem. A pattern recognition system has to be able to recognise objects invariant to scale, translation, and rotation. Presumably the human eye implements some of these preprocessing transforms in making sense of incoming stimuli, for example, placing signals onto a log scale. This paper surveys many of the commonly used invariance methods, and assesses their performance on one particular problem, estimating the quality of human motor imitation invariant to rotation, scale and translation.

Item Type:Preprint
Keywords:invariance, stacking, stacked, invariant
Subjects:Computer Science > Artificial Intelligence
Computer Science > Complexity Theory
ID Code:456
Deposited By: Kitts, Brendan
Deposited On:14 Jun 1998
Last Modified:11 Mar 2011 08:53


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