Automatic Modeling and Localization for Object Recognition
CARNEGIE-MELLON UNIV PITTSBURGH PA SCHOOL OF COMPUTER SCIENCE
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Being able to accurately estimate an objects pose location in an image is important for practical implementations and applications of object recognition. Recognition algorithms often trade off accuracy of the pose estimate for efficiency -- usually resulting in brittle and inaccurate recognition. One solution is object localization -- a local search for the objects true pose given a rough initial estimate of the pose. Localization is made difficult by the unfavorable characteristics for example, noise, clutter, occlusion and missing data of real images. In this thesis, we present novel algorithms for localizing 3D objects in 3D range-image data 3D-3D localization and for localizing 3D objects in 2D intensity-image data 3D-2D localization. Our localization algorithms utilize robust statistical techniques to reduce the sensitivity of the algorithms to the noise, clutter, missing data, and occlusion which are common in real images. Our localization results demonstrate that our algorithms can accurately determine the pose in noisy, cluttered images despite significant errors in the initial pose estimate. Acquiring accurate object models that facilitate localization is also of great practical importance for object recognition. In the past, models for recognition and localization were typically created by hand using computer-aided design CAD tools. Manual modeling suffers from expense and accuracy limitations. In this thesis, we present novel algorithms to automatically construct object-localization models from many images of the object. We present a consensus-search approach to determine which parts of the image justifiably constitute inclusion in the model. Using this approach, our modeling algorithms are relatively insensitive to the imperfections and noise typical of real image data. Our results demonstrate that our modeling algorithms can construct very accurate geometric models from rather noisy input data.
- Statistics and Probability