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2007

Visual novelty detection with automatic scale selection

8 years 2 months ago
Visual novelty detection with automatic scale selection
This paper presents experiments with an autonomous inspection robot, whose task was to highlight novel features in its environment from camera images. The experiments used two different attention mechanisms – saliency map and multi-scale Harris detector – and two different novelty detection mechanisms — the Grow-When-Required (GWR) neural network and an incremental Principal Component Analysis (PCA). For all mechanisms we compared fixed-scale image encoding with automatically scaled image patches. Results show that automatic scale selection provides a more efficient representation of the visual input space, but that performance is generally better using a fixed-scale image encoding. c 2007 Elsevier B.V. All rights reserved.
Hugo Vieira Neto, Ulrich Nehmzow
Added 27 Dec 2010
Updated 27 Dec 2010
Type Journal
Year 2007
Where RAS
Authors Hugo Vieira Neto, Ulrich Nehmzow
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