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Please use this identifier to cite or link to this item:
http://hdl.handle.net/1974/5399
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| Title: | Real-time Object Recognition in Sparse Range Images Using Error Surface Embedding |
| Authors: | Shang, LIMIN |
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| Keywords: | object recognition
range image |
| Issue Date: | 2010 |
| Series/Report no.: | Canadian theses |
| Abstract: | In this work we address the problem of object recognition and localization from
sparse range data. The method is based upon comparing the 7-D error surfaces of objects in various poses, which result from the registration error function between two convolved surfaces. The objects and their pose values are encoded by a small set of feature vectors extracted from the minima of the error surfaces. The problem of object recognition is thus reduced to comparing these feature vectors to find the corresponding error surfaces between the runtime data and a preprocessed database.
Specifically, we present a new approach to the problems of pose determination, object recognition and object class recognition. The algorithm has been implemented and tested on both simulated and real data. The experimental results demonstrate the technique to be both effective and efficient, executing at 122 frames per second on standard hardware and with recognition rates exceeding 97% for a database of 60 objects. The performance of the proposed potential well space embedding (PWSE) approach on large size databases was also evaluated on the Princeton Shape Bench-
mark containing 1,814 objects. In experiments of object class recognition with the Princeton Shape Benchmark, PWSE is able to provide better classification rates than
the previous methods in terms of nearest neighbour classification. In addition, PWSE
is shown to (i) operate with very sparse data, e.g., comprising only hundreds of points per image, and (ii) is robust to measurement error and outliers. |
| Description: | Thesis (Ph.D, Electrical & Computer Engineering) -- Queen's University, 2010-01-24 23:07:30.108 |
| URI: | http://hdl.handle.net/1974/5399 |
| Appears in Collections: | Queen's Theses & Dissertations
Electrical and Computer Engineering Graduate Theses
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