A Novel Image Correlation Matching Approach

Abstract

In this paper we present a novel approach which is combined local invariant feature descriptor named ARPIH (Angular Radial Partitioning Intensity Histogram) with histogram-based similar distance (HSD). The approach succeeds the ARPIH descriptor’s distinctive advantage and provides higher robustness in deformation image matching, such as rotation image, illumination changing image and perspective image, etc. Based on the MCD algorithm, we present the HSD algorithm. This algorithm transforms the image matching into the histogram matching by calculating the number of the similar points between template histogram and target histogram in order to decrease the calculation complicacy and improve the matching efficiency. A large amount groups of images are used in testing the approach presented in this paper. The matching results presented here indicate that the presented algorithm is efficient to figure out both the geometric deformation image matching and the illumination changing image matching. Contrast with the traditional matching algorithm, the approach presented in this paper has the obvious advantage of high matching precision, robustness and performance efficiency.