Stereo Matching Method using Directional Feature Vector
| Autor: | Jong-Hyun Jeon, Chul-Soo Ye, Chang-Gi Moon |
|---|---|
| Rok vydání: | 2007 |
| Předmět: |
Matching (statistics)
Pixel business.industry Template matching Feature vector ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION Pattern recognition Interval (mathematics) Geography Feature (computer vision) Computer Science::Computer Vision and Pattern Recognition Range (statistics) Computer vision Artificial intelligence business Second derivative |
| Zdroj: | Journal of Control, Automation and Systems Engineering. 13:52-57 |
| ISSN: | 1225-9845 |
| DOI: | 10.5302/j.icros.2007.13.1.052 |
| Popis: | In this paper we proposed multi-directional matching windows combined by multi-dimensional feature vector matching, which uses not only intensity values but also multiple feature values, such as variance, first and second derivative of pixels. Multi-dimensional feature vector matching has the advantage of compensating the drawbacks of area-based stereo matching using one feature value, such as intensity. We define matching cost of a pixel by the minimum value among eight multi-dimensional feature vector distances of the pixels expanded in eight directions having the interval of 45 degrees. As best stereo matches, we determine the two points with the minimum matching cost within the disparity range. In the experiment we used aerial imagery and IKONOS satellite imagery and obtained more accurate matching results than that of conventional matching method. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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