Visual novelty detection with automatic scale selection

• Autor(a) principal: Vieira Neto, Hugo
• Data de Publicação: 2007
• Outros Autores: Nehmzow, Ulrich
• Tipo de documento: Artigo
• Idioma: eng
• Título da fonte: Repositório Institucional da UTFPR (da Universidade Tecnológica Federal do Paraná (RIUT))
• Texto Completo: http://repositorio.utfpr.edu.br/jspui/handle/1/647
• topic: Robôs; Robótica; Visão por computador; Redes neurais (Computação); Robots; Robotics; Computer vision; Neural networks (Computer science)

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 — 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.