SwePub - search: WFRF:(Luk Chiu Hung)

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1.	Luk, Chiu-Hung, et al. (author) Vision-based hazard detection 2006 In: Intelligent Engineering Systems Through Artificial Neural Networks - Volume 16. - New York : ASME Press (American Society of Mechanical Engineers). ; , s. 6- Conference paper (peer-reviewed)abstract Advances in sensor and GPS technologies make possible better guidance tools for vehicle/aerial navigation. In the work reported here we look at the detection of hazards, such as aircraft or other objects, on or near the target runway. Our system consists of two modules: 1) regions of interest (ROI) detection, and 2) hazard recognition. One of the harder problems in object recognition is to segment the target object from a cluttered background. In this system we use a “poor man’s” segmentation, by taking advantage of the fact that we have an approximate reference that we can differentiate from. The regions of interest are defined as significant differences between the input image and the reference image. Since this differencing can be complex due to the fact that the images may not be precisely registered, we employed a novel histogram method which is reasonably invariant to spatial transformations for ROI detection.

Luk, Chiu-Hung, et al. (author)
Vision-based hazard detection
2006
In: Intelligent Engineering Systems Through Artificial Neural Networks - Volume 16. - New York : ASME Press (American Society of Mechanical Engineers). ; , s. 6-
Conference paper (peer-reviewed)abstract
- Advances in sensor and GPS technologies make possible better guidance tools for vehicle/aerial navigation. In the work reported here we look at the detection of hazards, such as aircraft or other objects, on or near the target runway. Our system consists of two modules: 1) regions of interest (ROI) detection, and 2) hazard recognition. One of the harder problems in object recognition is to segment the target object from a cluttered background. In this system we use a “poor man’s” segmentation, by taking advantage of the fact that we have an approximate reference that we can differentiate from. The regions of interest are defined as significant differences between the input image and the reference image. Since this differencing can be complex due to the fact that the images may not be precisely registered, we employed a novel histogram method which is reasonably invariant to spatial transformations for ROI detection.

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