The internationally popular scale-invariant feature transform (SIFT) algorithm and its improved algorithms are based on the difference-of-Gaussian (DoG) function for keypoint detection and description. However

the DoG function causes high-frequency image information loss

which leads to a sharp decline in matching performances along with increased image deformation. According to previous research on images in log-polar space

a new algorithm for keypoint detection and description in log-polar space is developed in this study. The new algorithm can completely reserve image information to overcome the drawbacks of the SIFT algorithm and its improved algorithms. The algorithm employed in this study converts the round image block centered on the sample point in Cartesian space into a rectangular image block in log-polar space and performs keypoint detection and descriptor extraction based on the derived rectangular image block. When performing keypoint detection

the proposed algorithm utilizes a window with a constant width that moves along the log axis of the radial gradient image in the log-polar space of the sample point to determine whether a sample point is to be defined as a keypoint and to calculate the character scales of the sample point. When a sample point is defined as a keypoint

the proposed algorithm performs descriptor extraction in the location of the character scale with a local maximum window response. The descriptor is a 192-dimensional vector that is based on the magnitude and orientation of the grayscale gradient of the rectangular image block in the log-polar space; it is invariant to changes in scale

orientation

and intensity. The SIFT algorithm

the speeded up robust feature (SURF) algorithm

and the proposed algorithm are compared based on the dataset and the performance evaluation indices proposed by Mikolajczyk. Results demonstrate that compared with SIFT and SURF algorithms

the proposed algorithm has significant advantages in the performance evaluation indices

such as correspondences

repeatability

correct matchs

and matching score. Classical image matching algorithms are based on Cartesian space; their matching performances for images with deformation

such as scale changing

are limited. This study formulates a new image matching algorithm based on log-polar space. First

the proposed algorithm converts the round image block centered on the sample point in Cartesian space into a rectangular image block in log-polar space. Thus

the proposed algorithm can effectively avoid high-frequency image information loss caused by the DoG function when performing keypoint detection. Second

the proposed algorithm extracts the descriptors of the keypoint based on the derived rectangular image block in log-polar space. This condition reduces the variance of images significantly. In sum

the proposed algorithm can significantly improve the performance of image matching by transforming an image in Cartesian space into one in log-polar space.