Feature Matching Based on Phase Congruency Corner Detection

Feature point matching is most commonly adopted among all kinds of stereo image matching.The result of feature point matching is affected by many factors such as object occlusions,lighting conditions and noises,therefore it is important to find a robust algorithm of feature point detection.In this paper described a new corner detector developed from the phase congruency model of feature detection.The new operator uses the principal moments of the phase congruency information to determine the corners and results in reliable feature detection under varying illumination conditions with fixed thresholds.A new feature point matching algorithm is then proposed.It employs the condition that the depth of the scene is locally continuous as extra constraint,and uses the method for extended assignment problem for global optimization.Experiments showed that the results of the algorithm are satisfactory.