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Performance evaluation of corner detectors using consistency and accuracy measures
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This paper presents a statistical approach for detecting corners from chain encoded digital arcs. An arc point is declared as a corner if the estimated parameters of the two fitted lines of the two arc segments immediately to the right and left of the arc point are statistically significantly different. The corner detection algorithm consists of two steps: corner detection and optimization. While corner detection involves statistically identifying the most likely corner points along an arc sequence, corner optimization deals with improving the locational errors of the detected corners.The major contributions of this research include developing a method for analytically estimating the covariance matrix of the fitted line parameters and developing a hypothesis test statistic to statistically test the difference between the parameters of two fitted lines. This paper discusses the theory and performance characterization of the proposed corner detector.