Jeremiah Neubert

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(a) (b) (c) Figure 1: Creation of 3D edge models directly from image sequences. The user indicates planar regions of an object to generate a polygon in (a). The planar regions are reconstructed, keyframes, consisting of sets of edgels (white lines) inside the planar region, are selected to capture viewpoint related changes in appearance (b). The resulting(More)
This paper presents the development of a vision-based neuro-fuzzy controller for a two axes gimbal system mounted on a small Un-manned Aerial Vehicle (UAV). The controller uses vision-based object detection as input and generates pan and tilt motion and velocity commands for the gimbal in order to keep the interest object at the center of the image frame. A(More)
This paper presents the development of a real time tracking algorithm that runs on a 1.2 GHz PC/104 computer onboard a small UAV. The algorithm uses zero mean normalized cross correlation to detect and locate an object in the image. A Kalman filter is used to make the tracking algorithm computationally efficient. Object position in an image frame is(More)
—This paper presents a visual tracking system that is capable of running real time on-board a small UAV (Unmanned Aerial Vehicle). The tracking system is computationally efficient and invariant to lighting changes and rotation of the object or the camera. Detection and tracking is autonomously carried out on the payload computer and there are two different(More)
This paper presents a method for rapidly generating crude, appearance-based edge models consisting of a set of planes. The appearance of each plane is modeled using a set of keyframes containing a list of edgels. These models are generated from a short video sequence with a few annotated frames indicating the location of the object of interest. The data(More)
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