Learn More
Feature description for local image patch is widely used in computer vision. While the conventional way to design local descriptor is based on expert experience and knowledge, learning-based methods for designing local descriptor become more and more popular because of their good performance and data-driven property. This paper proposes a novel data-driven(More)
—Although feature-based methods have been successfully developed in the past decades for the registration of optical images, the registration of optical and synthetic aperture radar (SAR) images is still a challenging problem in remote sensing. In this letter, an improved version of the scale-invariant feature transform is first proposed to obtain initial(More)
Local image descriptors are one of the key components in many computer vision applications. Recently, binary de-scriptors have received increasing interest of the community for its efficiency and low memory cost. The similarity of binary descriptors is measured by Hamming distance which has equal emphasis on all elements of binary descriptors. This paper(More)
Binary features have been incrementally popular in the past few years due to their low memory footprints and the efficient computation of Hamming distance between binary descriptors. They have been shown with promising results on some real time applications, e.g., SLAM, where the matching operations are relative few. However, in computer vision, there are(More)
  • 1