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

According to the traditional shape context algorithm within the image distortion, excessive noise points and lower matching rate issues, we present a Sequential Monte Carlo algorithm based on graph matching. First, the image feature points were evenly distributed to compute shape context information. Second, remaining points obtain a histogram of all the feature points by the shape context information. Using histogram function to calculate the cost of the square distance cost, were begin to match. Finally, structuring the graph model, using graphical models construct the affinity matrix; the matrix will be close to integer quadratic programming use the Sequential Monte Carlo algorithm to find out the optimal matching schemes. Experimental results show that: the proposed algorithm in image matching to ensure a high rate, while images of different perspectives and images in quite different conditions has good robustness and stableness.

Research on the Graph Matching based on Shape Context and Sequential Monte Carlo