Alignment of core point for shoeprint analysis and retrieval
Chia‐Hung Wei, Chih‐Ying Gwo · 2014
The purpose of this study is to propose a shoeprint retrieval method based on core point alignment for pattern analysis. The proposed method firstly detects contour points in a black-and-white shoeprint image. Those reliable contour points are selected to simulate the left and right sidelines of the shoeprint by curve fitting method. Subsequently, the most concave points along the left and right sidelines can determine the core point of the shoeprint, thereby partitioning the shoeprint into circular regions. Next, the Zernike moments of the circular regions are calculated for pattern description of each region. Finally, the Euclidean distance is measured to match the shoeprints with the same pattern. The highest = 0.726 is obtained from the first four Zernike moments with the radius = 90 pixels and three baselines. The experimental results also show that Zernike method in any orders always outperforms the compared moment invariants and GLCM method. This study has verified that the proposed method can effectively align the shoeprints for pattern comparison.