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

In this paper, we present a novel framework which significantly increases the accuracy of correspondences matching between two images under various image transformations. We first define a retina inspired patch-structure which mimics the human eye retina topology, and use the highly discriminative convolutional neural networks (CNNs) features to represent those patches. Then, we employ the conventional salient point methods to locate salient points, and finally, we fuse both the local descriptor of each salient point and the CNN feature from the local patch which the salient point belongs to. The evaluation results show the effectiveness of the proposed multiple features fusion (MFF) framework and that it improves the accuracy of leading approaches on two popular benchmark datasets.

Image Correspondences Matching Using Multiple Features Fusion