A Model of Saliency-Based Visual Attention for Rapid Scene Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
Feature Detection with Automatic Scale Selection
International Journal of Computer Vision
A self-organising network that grows when required
Neural Networks - New developments in self-organizing maps
Mobile Robotics: A Practical Introduction
Mobile Robotics: A Practical Introduction
Incremental PCA or On-Line Visual Learning and Recognition
ICPR '02 Proceedings of the 16 th International Conference on Pattern Recognition (ICPR'02) Volume 3 - Volume 3
Scale & Affine Invariant Interest Point Detectors
International Journal of Computer Vision
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
Editorial: Mobile robotics in the UK and worldwide: Fast changing, and as exciting as ever
Robotics and Autonomous Systems
An automated vision based on-line novel percept detection method for a mobile robot
Robotics and Autonomous Systems
Fractal analogies for general intelligence
AGI'12 Proceedings of the 5th international conference on Artificial General Intelligence
Self-organizing maps with a time-varying structure
ACM Computing Surveys (CSUR)
Robotics and Autonomous Systems
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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.