3 research outputs found
Recovering a boundary-level structural description from dynamic stereo
We present a stereo algorithm to recursively compute a boundary-level structural description of a static scene, from a sequence of dynamic stereo images. This algorithm is based on connected line segments as the basic match primitive, which yields a description composed primarily of boundaries of objects in the scene. The algorithm is integrated into a dynamic stereo vision system to compute and incrementally refine such a structural description recursively, using belief measures. The approach is illustrated with a real dynamic stereo sequence.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/30120/1/0000496.pd
Information Fusion for Improved Motion Estimation
studentship award number 98318229Motion Estimation is an important research field with many commercial applications including
surveillance, navigation, robotics, and image compression. As a result, the field has received
a great deal of attention and there exist a wide variety of Motion Estimation techniques which
are often specialised for particular problems. The relative performance of these techniques, in
terms of both accuracy and of computational requirements, is often found to be data dependent,
and no single technique is known to outperform all others for all applications under all
conditions. Information Fusion strategies seek to combine the results of different classifiers or
sensors to give results of a better quality for a given problem than can be achieved by any single
technique alone. Information Fusion has been shown to be of benefit to a number of applications
including remote sensing, personal identity recognition, target detection, forecasting, and
medical diagnosis.
This thesis proposes and demonstrates that Information Fusion strategies may also be applied
to combine the results of different Motion Estimation techniques in order to give more robust,
more accurate and more timely motion estimates than are provided by any of the individual
techniques alone.
Information Fusion strategies for combining motion estimates are investigated and developed.
Their usefulness is first demonstrated by combining scalar motion estimates of the frequency
of rotation of spinning biological cells. Then the strategies are used to combine the results from
three popular 2D Motion Estimation techniques, chosen to be representative of the main approaches
in the field. Results are presented, from both real and synthetic test image sequences,
which illustrate the potential benefits of Information Fusion to Motion Estimation applications.
There is often a trade-off between accuracy of Motion Estimation techniques and their computational
requirements. An architecture for Information Fusion that allows faster, less accurate
techniques to be effectively combined with slower, more accurate techniques is described.
This thesis describes a number of novel techniques for both Information Fusion and Motion
Estimation which have potential scope beyond that examined here. The investigations presented
in this thesis have also been reported in a number of workshop, conference and journal papers,
which are listed at the end of the document
Untersuchung und Entwicklung von Algorithmen zur Stereobildauswertung für die Erfassung von Objekten im Umfeld von Fahrzeugen und Realisierung einer Hindernisdetektion in Echtzeit mittels einer Hardwareimplementierung auf einem FPGA
Magdeburg, Univ., Fak. für Elektrotechnik und Informationstechnik, Diss., 2009von Michael Torno