TITLE:
Corner-Based Image Alignment using Pyramid Structure with Gradient Vector Similarity
AUTHORS:
Chin-Sheng Chen, Kang-Yi Peng, Chien-Liang Huang, Chun-Wei Yeh
KEYWORDS:
Corner-Based Image Alignment; Corner Detection; Edge-Based Template Matching; Gradient Vector
JOURNAL NAME:
Journal of Signal and Information Processing,
Vol.4 No.3B,
October
17,
2013
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.