Modeling visual attention via selective tuning
Artificial Intelligence - Special volume on computer vision
Event detection from time series data
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Motion Understanding: Task-Directed Attention and Representations that Link Perception with Action
International Journal of Computer Vision
Object-based visual attention for computer vision
Artificial Intelligence
Models of bottom-up and top-down visual attention
Models of bottom-up and top-down visual attention
Motion Analysis with Application to Assistive Vision Technology
ICTAI '04 Proceedings of the 16th IEEE International Conference on Tools with Artificial Intelligence
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
Salient region detection using weighted feature maps based on the human visual attention model
PCM'04 Proceedings of the 5th Pacific Rim Conference on Advances in Multimedia Information Processing - Volume Part II
A hierarchical approach to color image segmentation using homogeneity
IEEE Transactions on Image Processing
Multiscale gradient watersheds of color images
IEEE Transactions on Image Processing
Combined morphological-spectral unsupervised image segmentation
IEEE Transactions on Image Processing
IEEE Transactions on Circuits and Systems for Video Technology
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This paper proposes a framework, based on a spatio-temporal attentive mechanism, for automatic region-of-interest determination, corresponding to events in video sequences of natural scenes of dynamic environments. We view this work as a preliminary step towards the solution of high-level semantic event analysis. More specifically, we wish to detect a visual event within a cluttered scene, without intensive training algorithms. In contrast to event detection methods used in the literature, which drive attention based on motion and spatial location hypothesis, in our approach the visual attention is region-driven as well as feature-driven. For this purpose, a two stages attention mechanism is proposed. In a first phase, spatio-temporal activity analysis extracts key-frames from the image sequence and selects salient areas within these frames. The three types of visual attention features are used, namely, intensity, color and motion. Consequently, the selected areas are further processed to determine the most active region, based on a newly defined region saliency measure. Qualitative and quantitative results, using the proposed framework, are illustrated envisaging the application domain of change detection in automated visual surveillance.