In a feature based system physical objects are represented as spatial groups of features. Systems which hope to operate on objects must make associations between features that belong on the same physical object. This paper segments interest points in individual frames of an image sequence using motion models based on image transformations. Experiments evaluate the associations made by these segments against ground truth data. We give an improved version of the existing algorithm which can lead to easier threshold selection in some systems although the ideal threshold is shown to depend on the goal of the segmentation. Lastly we show that the underlying motion of the object is not the only factor in determining the performance of the segmentation.
|Commitee:||Beveridge, Ross, Hayne, Stephen|
|School:||Colorado State University|
|School Location:||United States -- Colorado|
|Source:||MAI 49/03M, Masters Abstracts International|
|Keywords:||Computer vision, Feature association, Motion segmentation|
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