A Research Of Contour-based Corner Detection Algorithms

After decades of development,until now corner detection algorithms can be devided into two kinds: intensity based methods and contour based methods.Compared with intensity based ones,contour based methods reveal their superiority in corner localization accuracy and earn lots of attention.In recent decades,researchers proposed a lot of contour based corner detectors,in which CSS(Curvature Scale Space)algorithm is the most popular one,and there are many algorithms derived from CSS method.However,most of them utilize curvature of contour to measure the corner response value,resulting in the sensitivity to the noise.Hence,many researchers try to explore other measures to calculate the corner response function,such as CPDA,CTAR and DoG methods.Although these methods solve the deficiency of CSS based algorithms in some extent,there are other kinds of defects,such as the low computational efficiency.So in this thesis we systematically introduce the characteristics of the state-of-theart contour-based corner detectors and analyze the advantages and disadvantages of them.We make academic contributions in two aspects:(1)Propose the ACRA corner detector based on CPDA algorithm,successfully increasing the corner detection performance and computational efficiency.(2)Propose two original corner detector SODC-M and SODC-E based on second order difference of contour in the image.We analyze the regularity of second order difference map of contour and utilize Manhattan distance and Euclidean distance to measure the corner response value respectively in the two methods.Compared with other nine corner detectors,our SODC-M achieves higher computational efficiency and our SODC-E obtains higher detection performance.