FOMP: A Novel Preprocessing Technique to Speed-Up the Outlier Removal from Matched Points
| Autor: | Jonathan S. Ramos, Carolina Y. V. Watanabey, Agma J. M. Traina |
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| Rok vydání: | 2016 |
| Předmět: |
Computer science
business.industry Feature extraction 0211 other engineering and technologies Point set registration Pattern recognition 02 engineering and technology Euclidean distance Pattern recognition (psychology) Outlier 0202 electrical engineering electronic engineering information engineering Preprocessor 020201 artificial intelligence & image processing Point (geometry) Computer vision Artificial intelligence business Time complexity 021101 geological & geomatics engineering |
| Zdroj: | SIBGRAPI |
| DOI: | 10.1109/sibgrapi.2016.039 |
| Popis: | Image matching plays a major role in many applications, including pattern recognition and biomedical imaging. It encompasses three steps: 1) interest point selection, 2) feature extraction from each interest point, 3) features point matching. For steps 1 and 2, traditional interest point detectors/extractors have worked well. However, for step 3 even a few points incorrectly matched (outliers), might lead to an undesirable result. State-of-the-art consensus algorithms present a high time cost as the number of outlier increases. Aimed at overcoming this problem, we present FOMP, a novel preprocessing approach, that reduces the amount of outliers in the initial set of matched points by filtering out the vertices that present a higher difference among their edges in a complete graph representation of the points. The precision of traditional methods is kept, while the time is speed up in 50%. The approach removes, in average, more than 65% of outliers, while keeping over 98% of the inliers. |
| Databáze: | OpenAIRE |
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