Abstract
Matching of keypoints across image patches forms the basis of computer vision applications, such as object detection, recognition, and tracking in real-world images. Most of keypoint methods are mainly used to match the high-resolution images, which always utilize an image pyramid for multiscale keypoint detection. In this paper, we propose a novel keypoint method to improve the matching performance of image patches with the low-resolution and small size. The location, scale, and orientation of keypoints are directly estimated from an original image patch using a Log-Spiral sampling pattern for keypoint detection without consideration of image pyramid. A Log-Spiral sampling pattern for keypoint description and two bit-generated functions are designed for generating a binary descriptor. Extensive experiments show that the proposed method is more effective and robust than existing binary-based methods for image patch matching.
Highlights
Image matching based on sparse number of keypoints has been intensively studied in the community of computer vision
How to improve the robust matching of image patches with low-resolution and small size has been a concerned problem in the community of computer vision due to the recent increasing application such as mobile video device, wireless sensor network, and the Internet of things
Our descriptor relies on the Log-Spiral sampling pattern for keypoint description to capture more information around a keypoint and two bit-generated functions to enhance the independence between descriptors
Summary
Image matching based on sparse number of keypoints has been intensively studied in the community of computer vision. Most methods of FAST-based keypoint detection and binary-based keypoint description (such as ORB, BRISK, and FREAK) focus on fast matching of highresolution images. If the keypoint based mobile device whose built-in video camera captures images of low resolution is placed in a more complex environment, more advanced keypoint algorithms are necessary for the mobile device to carry out its scene and landmark recognition tasks It is widely used in different applications, such as detection and recognition of objects in Cluttered Scenes [1, 2], matching two images with different resolutions [3], and visual saliency detection based on region descriptors [4]. The usefulness and weakness of SIFT and its variants in low-resolution image matching were analyzed, a method to enhance the matching performance of image patches was not presented. Multiscale keypoint detection based on image pyramid and binary descriptor generation based on sampling pattern with concentric circle structure did not have a significant effect on image patch matching. Our method outperforms all of binary-based methods in terms of recall and number of correct matches
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