A feature-based matching scheme: MPCD and robust matching strategy

This paper presents a scheme that matches interest point features detected on two images taken from different points of view. To accomplish this objective, we jointly consider the corner detection and matching problems. Firstly, a new multi-scale Plessey corner detector (MPCD) is used to detect the interest points. Secondly, the geometric constraint between two images is exploited, based on which we propose a new energy function that can approximate 2D affine transformation in a more efficient way. Only a small set of corners with the highest accuracy and robustness are considered in the first stage, consequently, a small data space is provided to the robust algorithm in the second stage reducing the computation time. Therefore, more information can be incorporated into our scheme based on corner detection and matching phases. We compare our method using the proposed MPCD with two standard corner detectors based on image matching. We also evaluate our proposed matching strategy against Zhengyou’s method [Deriche, R., Zhang, Z., Luong, Q.-T., Faugeras, O., 1994. Robust recovery of the epipolar geometry for an uncalibrated stereo rig. In: European Conference on Computer Vision, Stockholm, Sweden, pp. 567–576]. Our scheme provides a new viewpoint and better results for the traditional feature matching problem.