Abstract
The keypoint detection and its description are two critical aspects of local keypoints matching which is vital in some computer vision and pattern recognition applications. This paper presents a new scale-invariant and rotation-invariant detector and descriptor, coined, respectively, DDoG and FBRK. At first the Hilbert curve scanning is applied to converting a two-dimensional (2D) digital image into a one-dimensional (1D) gray-level sequence. Then, based on the 1D image sequence, an approximation of DoG detector using second-order difference-of-Gaussian function is proposed. Finally, a new fast binary ratio-based keypoint descriptor is proposed. That is achieved by using the ratio-relationships of the keypoint pixel value with other pixel of values around the keypoint in scale space. Experimental results show that the proposed methods can be computed much faster and approximate or even outperform the existing methods with respect to performance.
Highlights
Local keypoints matching is finding corresponding points between two or more images of the same scene or object
Based on the 1D image sequence, we propose an approximation of DoG detector by using second-order difference-of-Gaussian function, coined DDoG detector
The experiment results show that our proposed method has leading performance under image blur, light, and JPEG compression and is comparable to other competitors for viewpoint and scale changes, while for rotation changes our method is slightly weaker than scale-invariant feature transform (SIFT) descriptor and better than other descriptors
Summary
Local keypoints matching is finding corresponding points between two or more images of the same scene or object. In addition to PCASIFT and GLOH, Bay et al presented a scale-invariant and rotation-invariant keypoint descriptor using integral images for image convolutions, which combines a keypoint detector and a descriptor called SURF [20]. These extensions focused primarily on improving the matching performances. Inspired by the above presented detectors and descriptors, this paper proposed a new scheme for keypoint detection and description. In our proposed keypoint detector, the first important step is to convert a 2D digital image into a 1D gray-level sequence by Hilbert curve scanning.
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