No description available.

No description available.

No description available.

Thesis (M.A.)--University of Hong Kong, 1997. / Includes bibliographical references (leaf 262-263).

Thesis (M.A.)--University of Hong Kong, 2000. / Includes bibliographical references (leaves 55-57).

Thesis (M.A.)--University of Hong Kong, 1997. / Includes bibliographical references (leaf 262-263). Also available in print.

Thesis (M.A.)--University of Hong Kong, 2000. / Includes bibliographical references (leaves 55-57). Also available in print.

Techn. University, Diss., 2004--Berlin.

Objects can exhibit different dynamics at different scales, a property that isoftenexploited by visual tracking algorithms. A local dynamicmodel is typically used to extract image features that are then used as inputsto a system for tracking the entire object using a global dynamic model.Approximate local dynamicsmay be brittle---point trackers drift due to image noise and adaptivebackground models adapt to foreground objects that becomestationary---but constraints from the global model can make them more robust.We propose a probabilistic framework for incorporating globaldynamics knowledge into the local feature extraction processes.A global tracking algorithm can beformulated as a generative model and used to predict feature values thatinfluence the observation process of thefeature extractor. We combine such models in a multichain graphicalmodel framework.We show the utility of our framework for improving feature tracking and thusshapeand motion estimates in a batch factorization algorithm.We also propose an approximate filtering algorithm appropriate for onlineapplications, and demonstrate its application to problems such as backgroundsubtraction, structure from motion and articulated body tracking.

The official U.S. standards for corn have been available for almost one hundred years. Corn grading system has been gradually updated over the years. In this thesis, we investigated a fast corn grading system, which includes the mechanical part and the computer recognition part. The mechanical system can deliver the corn kernels onto the display plate. For the computer recognition algorithms, we extracted common features from each corn kernel, and classified them to measure the grain quality.