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On the Parameter Estimation Accuracy of Model Matching Feature Detectors
| Content Provider | Semantic Scholar |
|---|---|
| Author | Baker, Simon |
| Copyright Year | 1997 |
| Abstract | The performance of model tting feature detectors is critically dependent upon the function used to measure the degree of t between the feature model and the image data In this paper we consider the class of weighted L norms as potential tting functions and study the e ect which the choice of tting function has on one particular aspect of performance namely parameter estimation accuracy We rst derive an optimality criterion based upon how far an ideal feature instance is perturbed around the feature manifold when noise is added to it We then show that a rst order linear approximation to the feature manifold results in the Euclidean L norm being optimal We next show empirically that for non linear manifolds the Euclidean L norm is no longer in general optimal Finally we present the results of several experiments comparing the performance of various weighting functions on a number of ubiquitous features |
| File Format | PDF HTM / HTML |
| Alternate Webpage(s) | https://academiccommons.columbia.edu/doi/10.7916/D8MC96T2/download |
| Alternate Webpage(s) | http://www.ri.cmu.edu/pub_files/pub2/baker_simon_1997_2/baker_simon_1997_2.pdf |
| Language | English |
| Access Restriction | Open |
| Subject Keyword | Detectors Ectomesenchymal Chondromyxoid Tumor Estimation theory Experiment Feature model Linear approximation Nonlinear system Optimality criterion Population Parameter manifold |
| Content Type | Text |
| Resource Type | Article |
