A Reliable Distribution Quality Measure in Image Registration

Abstract

The accuracy of feature-based image registration methods significantly depends on the number of extracted key-points and the quality of their distribution. Therefore, it is important to examine the quality of distribution of key-points by using a suitable measure. However, in literature, examining distribution of key-points could not get appropriate attention. Very few metrics have been reported that discussed about the distribution quality of the feature points. These metrics consider all detected key-points inliers (correctly matched key-points). Therefore, they may provide reasonable distribution quality measure in the absence of outliers. However, in the presence of outliers, these measures are not compliance with accuracy and may provide misleading quantities. In this paper, we propose a distribution quality metric that can provide a reliable measure for distribution and overcomes the limitations in the existing measures. The proposed measure uses area and shape of Delaunay triangles and incorporates the goodness of the key-points. Experimental results show that the proposed measure can evaluate distribution quality accurately even in the presence of outliers. It is also well-compliance with the registration accuracy.