Context-free attentional operators: the generalized symmetry transform
International Journal of Computer Vision - Special issue on qualitative vision
Local Grayvalue Invariants for Image Retrieval
IEEE Transactions on Pattern Analysis and Machine Intelligence
SUSAN—A New Approach to Low Level Image Processing
International Journal of Computer Vision
Analysis of gray level corner detection
Pattern Recognition Letters
Robust detection of significant points in multiframe images
Pattern Recognition Letters
Algorithms for Defining Visual Regions-of-Interest: Comparison with Eye Fixations
IEEE Transactions on Pattern Analysis and Machine Intelligence
Data- and Model-Driven Gaze Control for an Active-Vision System
IEEE Transactions on Pattern Analysis and Machine Intelligence
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Learning to Detect Natural Image Boundaries Using Local Brightness, Color, and Texture Cues
IEEE Transactions on Pattern Analysis and Machine Intelligence
Focus-of-Attention from Local Color Symmetries
IEEE Transactions on Pattern Analysis and Machine Intelligence
Context-Based Segmentation of Image Sequences
IEEE Transactions on Pattern Analysis and Machine Intelligence
Towards automatic visual obstacle avoidance
IJCAI'77 Proceedings of the 5th international joint conference on Artificial intelligence - Volume 2
Unsupervised image categorization
Image and Vision Computing
A Spatio-temporal Extension of the SUSAN-Filter
ICANN '08 Proceedings of the 18th international conference on Artificial Neural Networks, Part I
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The detection of basic events such as turning points in object trajectories is an important low-level task of image sequence analysis. We propose extending the SUSAN algorithm to the spatio-temporal domain for a context-free detection of salient events, which can be used as a starting point for further motion analysis. While in the static 2Dcase SUSAN returns a map indicating edges and corners, we obtain in a straight forward extension of SUSAN a 2D+1D saliency map indicating edges and corners in both space and time. Since the mixture of spatial and temporal structures is still unsatisfying, we propose a modification better suited for event analysis.