Corner detection with covariance propagation

This paper presents a statistical approach for detecting corners from chain encoded digital arcs. An are point is declared as a corner if the estimated parameters of the two lifted lines of the two are segments immediately to the right and left of the are 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 corn points along an are 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. Performance evaluation study showed that the algorithm is robust and accurate for complex images. if has an average misdetection rate of 2.5% and false alarm rate of 2.2% for the complex RADIUS images. This paper discusses the theory and performance characterization of the proposed corner detector.