Visual attention and novelty detection: experiments with automatic scale selection
Year: 2006
Type: conference-paper
Abstract: We present experiments with an autonomous inspection robot, whose task was to highlight novel features in its environment using camera images. Experiments were conducted with two different attention mechanisms — saliency map and multiscale Harris detector — and two different novelty detection mechanisms — the Grow-When-Required neural network and incremental PCA. For both mechanisms we compared fixed-scale image encoding with automatically scaled image patches. Results show that using 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. 1.
Authors: Ulrich Nehmzow, Hugo Vieira Neto
Institution: University of Essex
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W2120494318