Computer Vision and Image Understanding - Special issue on event detection in video
Graphical pattern identification inspired by perception
ACIIDS'11 Proceedings of the Third international conference on Intelligent information and database systems - Volume Part II
Weighted map for reflectance and shading separation using a single image
ACCV'09 Proceedings of the 9th Asian conference on Computer Vision - Volume Part III
Applying distribution of feature points to detect multiple plane
International Journal of Grid and Utility Computing
The invariance properties of chromatic characteristics
PSIVT'06 Proceedings of the First Pacific Rim conference on Advances in Image and Video Technology
Keeping the vehicle on the road: A survey on on-road lane detection systems
ACM Computing Surveys (CSUR)
Signal segmentation using changing regression models with application in seismic engineering
Digital Signal Processing
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Detecting critical changes of environments while driving is an important task in driver assistance systems. In this paper, a computational model motivated by human cognitive processing and selective attention is proposed for this purpose. The computational model consists of three major components, referred to as the sensory, perceptual, and conceptual analyzers. The sensory analyzer extracts temporal and spatial information from video sequences. The extracted information serves as the input stimuli to a spatiotemporal attention (STA) neural network embedded in the perceptual analyzer. If consistent stimuli repeatedly innervate the neural network, a focus of attention will be established in the network. The attention pattern associated with the focus, together with the location and direction of motion of the pattern, form what we call a categorical feature. Based on this feature, the class of the attention pattern and, in turn, the change in driving environment corresponding to the class are determined using a configurable adaptive resonance theory (CART) neural network, which is placed in the conceptual analyzer. Various changes in driving environment, both in daytime and at night, have been tested. The experimental results demonstrated the feasibilities of both the proposed computational model and the change detection system.