• The Korean Journal of Applied Statistics
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• v.20 no.1
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• pp.147-157
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• 2007

Edge detection has been widely used in computer vision and image processing. We describe a new edge detector based on the robust rank-order test which is a useful alternative to Wilcoxon test. Our method is based on detecting pixel intensity changes between two neighborhoods with a $r{\times}r$ window using an edge-height model to perform effectively on noisy images. Some experiments of our robust rank-order detector with several existing edge detectors are carried out on both synthetic images and real images with and without noise.

• 한국데이터정보과학회:학술대회논문집
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• 2004.04a
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• pp.197-201
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• 2004

We describe a new edge detector based on the robust rank-order (RRO) test which is a useful alternative to Wilcoxon test, using $r{\times}r$ window for detecting edges of all possible orientations in noisy images. Some experiments of statistical edge detectors based on the Wilcoxon test and T test with our RRO detector are carried out on synthetic and real images corrupted by both Gaussian and impulse noise. We also implement these edge detectors using Java on the Web.

• The Korean Journal of Applied Statistics
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• v.24 no.1
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• pp.137-143
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• 2011

The classification of subjects with unknown distribution in a small sample size often involves order-restricted constraints in multivariate parameter setups. Those problems make the optimality of a conventional likelihood ratio based statistical inferences not feasible. Fortunately, Roy (1953) introduced union-intersection principle(UIP) which provides an alternative avenue. Multivariate linear rank statistics along with that principle, yield a considerably appropriate robust testing procedure. Furthermore, conditionally distribution-free test based upon exact permutation theory is used to generate p-values, even in a small sample. Applications of this method are illustrated in a real microarray data example (Lobenhofer et al., 2002).