A Model of Saliency-Based Visual Attention for Rapid Scene Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
Robust Real-Time Face Detection
International Journal of Computer Vision
An unsupervised method for clustering images based on their salient regions of interest
MULTIMEDIA '06 Proceedings of the 14th annual ACM international conference on Multimedia
Illumination-robust variational optical flow with photometric invariants
Proceedings of the 29th DAGM conference on Pattern recognition
Salient region detection and segmentation
ICVS'08 Proceedings of the 6th international conference on Computer vision systems
Is bottom-up attention useful for object recognition?
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
SURF: speeded up robust features
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part I
Applied Computational Intelligence and Soft Computing - Special issue on Awareness Science and Engineering
Robotics and Autonomous Systems
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In this work we contribute to development of an online unsupervised technique allowing learning of objects from unlabeled images and their detection when seen again. We were inspired by early processing stages of human visual system and by existing work on human infants learning. We suggest a novel fast algorithm for detection of visually salient objects, which is employed to extract objects of interest from images for learning. We demonstrate how this can be used in along with state-of-the-art object recognition algorithms such as SURF and Viola-Jones framework to enable a machine to learn to re-detect previously seen objects in new conditions. We provide results of experiments done on a mobile robot in common office environment with multiple every-day objects.