Self-organization and associative memory: 3rd edition
Self-organization and associative memory: 3rd edition
International Journal of Computer Vision
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
Recognition without Correspondence using MultidimensionalReceptive Field Histograms
International Journal of Computer Vision
IEEE Transactions on Pattern Analysis and Machine Intelligence
ECCV '96 Proceedings of the 4th European Conference on Computer Vision-Volume II - Volume II
Environment-specific novelty detection
ICSAB Proceedings of the seventh international conference on simulation of adaptive behavior on From animals to animats
A self-organising network that grows when required
Neural Networks - New developments in self-organizing maps
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
Novelty detection: a review—part 1: statistical approaches
Signal Processing
Novelty detection: a review—part 2: neural network based approaches
Signal Processing
An Approach to Novelty Detection Applied to the Classification of Image Regions
IEEE Transactions on Knowledge and Data Engineering
Evolving novelty detectors for specific applications
Neurocomputing
Autonomous Mapping Using a Flexible Region Map for Novelty Detection
ICIRA '09 Proceedings of the 2nd International Conference on Intelligent Robotics and Applications
An automated vision based on-line novel percept detection method for a mobile robot
Robotics and Autonomous Systems
Review: A review of novelty detection
Signal Processing
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We present a framework to perform novelty detection using visual input in which a mobile robot first learns a model of normality in its operating environment and later uses this to highlight uncommon visual features that may appear. This ability is of great importance for both robotic exploration and inspection tasks, because it enables the robot to allocate computational and attentional resources efficiently to those features which are novel. At the heart of the proposed system is the image encoding mechanism which uses local colour statistics from regions selected by a biologically-inspired model of visual attention. Our approach works in real-time with a wide, unrestricted field of view and is robust to image transformations. Experiments conducted in an engineered scenario demonstrate the efficiency and functionality of our method.