2 research outputs found

    Motion detection in spatio-temporal space

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    We present an analysis of existing motion detectors for determining desirable characteristics of a motion detector. A spatio-temporal surface type inseparable model is then proposed for motion detection. Based on this model, we analyzed mathematically how the geometry of the intensity hypersurface gives information about motion in image. The local motion information, obtained from the parameters of the Monge patch approximating the intensity hypersurface in the spatio-temporal space, may be used for segmentation of dynamic scenes. Motion detection results for real sequences show the robustness of this detector.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/28157/1/0000609.pd

    A model-based approach for detection of runways and other objects in image sequences acquired using an on-board camera

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    This research was initiated as a part of the Advanced Sensor and Imaging System Technology (ASSIST) program at NASA Langley Research Center. The primary goal of this research is the development of image analysis algorithms for the detection of runways and other objects using an on-board camera. Initial effort was concentrated on images acquired using a passive millimeter wave (PMMW) sensor. The images obtained using PMMW sensors under poor visibility conditions due to atmospheric fog are characterized by very low spatial resolution but good image contrast compared to those images obtained using sensors operating in the visible spectrum. Algorithms developed for analyzing these images using a model of the runway and other objects are described in Part 1 of this report. Experimental verification of these algorithms was limited to a sequence of images simulated from a single frame of PMMW image. Subsequent development and evaluation of algorithms was done using video image sequences. These images have better spatial and temporal resolution compared to PMMW images. Algorithms for reliable recognition of runways and accurate estimation of spatial position of stationary objects on the ground have been developed and evaluated using several image sequences. These algorithms are described in Part 2 of this report. A list of all publications resulting from this work is also included
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