Least-Squares Fitting of Two 3-D Point Sets
IEEE Transactions on Pattern Analysis and Machine Intelligence
Simplifying surfaces with color and texture using quadric error metrics
Proceedings of the conference on Visualization '98
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
A Principled Approach to Detecting Surprising Events in Video
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Local visual homing by matched-filter descent in image distances
Biological Cybernetics
An iterative image registration technique with an application to stereo vision
IJCAI'81 Proceedings of the 7th international joint conference on Artificial intelligence - Volume 2
SURF: speeded up robust features
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part I
Image change detection algorithms: a systematic survey
IEEE Transactions on Image Processing
Survey of image-based representations and compression techniques
IEEE Transactions on Circuits and Systems for Video Technology
Efficient camera-based pose estimation for real-time applications
IROS'09 Proceedings of the 2009 IEEE/RSJ international conference on Intelligent robots and systems
Semantic interpretation of novelty in images using histograms of oriented gradients
ICIRA'12 Proceedings of the 5th international conference on Intelligent Robotics and Applications - Volume Part III
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One important feature of a cognitive system is to perceive and understand its environment and to adapt its actions to changes and unforeseen situations. In this paper, we propose a scheme for visual surprise detection in cognitive mobile robots. With the robot's observation and a set of reference images previously acquired near its current viewpoint, a pixel-wise surprise trigger is computed using Bayesian probabilistic inference techniques. With appropriate mathematical approximations this algorithm can be implemented on modern graphics hardware which nearly allows for real-time surprise detection. In order to refer to prior observations, a mobile robot has to be able to re-localize itself with respect to its environment. Thus, we also present two online image-based homing algorithms which both facilitate the computation of location-independent surprise triggers. Experiments show acceptable results in terms of robust and fast detection of unexpected changes in the environment.