Abstract
This paper presents a corner-based image alignment algorithm based on the procedures of corner-based template matching and geometric parameter estimation. This algorithm consists of two stages: 1) training phase, and 2) matching phase. In the training phase, a corner detection algorithm is used to extract the corners. These corners are then used to build the pyramid images. In the matching phase, the corners are obtained using the same corner detection algorithm. The similarity measure is then determined by the differences of gradient vector between the corners obtained in the template image and the inspection image, respectively. A parabolic function is further applied to evaluate the geometric relationship between the template and the inspection images. Results show that the corner-based template matching outperforms the original edge-based template matching in efficiency, and both of them are robust against non-liner light changes. The accuracy and precision of the corner-based image alignment are competitive to that of edge-based image alignment under the same environment. In practice, the proposed algorithm demonstrates its precision, efficiency and robustness in image alignment for real world applications.
Highlights
This paper presents a corner-based image alignment algorithm based on the procedures of corner-based template matching and geometric parameter estimation
The similarity measure is determined by the differences of gradient vector between the corners obtained in the template image and the inspection image, respectively
Results show that the corner-based template matching outperforms the original edge-based template matching in efficiency, and both of them are robust against non-liner light changes
Summary
In automatic optical inspection (AOI) systems, speed, precision, and robustness are the key requirements in real world applications. In the area-based template matching, normalized cross correlation (NCC) method is a popular one that can be used to evaluate the degree of similarity between template and scene images. It is not robust against non-liner light changes and occluded objects [2]. To adapt the complicated environment for template matching, Steger [4] presented a similarity measure based on the difference of gradient direction in edges that is robust against non-linear illumination changes, and it can be utilized to recognize occluded objects. Tran’s method has been adopted in the cornerbased template matching because of its efficiency and robustness for corner detection
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