English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

A fast downward-viewing scene matching method, taking airports, oil depots, harbors and so on as research objects, is proposed in this article which is based on the visual saliency detection and the segmentation of frequency domain. According to the characteristics of downward-viewing images, such as high resolution and complex background texture, saliency detection is used to determine the candidate region where the target may exist to reduce the searching range effectively. And then, the segmentation of frequency domain is used to eliminate the frequency component except the frequency of the target to reduce the redundant information, thereby saving the computation of SIFT feature extraction and matching. A variety of experiments under different interference factors are carried out base on the typical object database of downward-viewing images in this paper. Experimental results show that the fast matching algorithm proposed in this paper can not only maintain the validity of SIFT features under the condition of rotation, scale, illumination and viewpoint changes, but also shorten the matching time largely and improve the matching efficiency, laying the foundation for further practical application. Keywords-Spectral residual; Saliency detection; SIFT matching; Segmentation of frequency domain

Fast SIFT scene matching algorithm based on saliency detection and frequency segmentation for downward-viewing images