Vision-based hazard detection

Luk, Chiu-HungDepartment of Computer and Electrical Engineering, OGI School of Science and Engineering (författare)

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.

Zaveri, Mazad S.Department of Electrical and Computer Engineering, Portland State University (författare)
Hammerstrom, DanDepartment of Electrical and Computer Engineering, Portland State University (författare)
Kerr, Richard J.Portland, USA (författare)
Department of Computer and Electrical Engineering, OGI School of Science and EngineeringDepartment of Electrical and Computer Engineering, Portland State University (creator_code:org_t)

Ingår i:Intelligent Engineering Systems Through Artificial Neural Networks - Volume 16New York : ASME Press (American Society of Mechanical Engineers), s. 6-

Av författaren/redakt...: Luk, Chiu-Hung; Zaveri, Mazad S.; Hammerstrom, Dan; Kerr, Richard J.

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