Graphics and Image Local Deformation Based on Moving Least Squares Method
Vol: 27, Issue: 4, Page: 816-823
2020
- 60Usage
Metric Options: CountsSelecting the 1-year or 3-year option will change the metrics count to percentiles, illustrating how an article or review compares to other articles or reviews within the selected time period in the same journal. Selecting the 1-year option compares the metrics against other articles/reviews that were also published in the same calendar year. Selecting the 3-year option compares the metrics against other articles/reviews that were also published in the same calendar year plus the two years prior.
Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Metrics Details
- Usage60
- Downloads57
- Abstract Views3
Artifact Description
Abstract: Combined with Moving Least Squares method, a deformation function based on control points and area was defined. A localization processing method for image deformation was proposed which extended to vector graph deformation. After adding control points to control deformation and setting the target deformation zone as control area, control points were controlled to deform image and vector graph, which could produce affine transformation, similarity transformation and rigid transformation results. Experiments show that, this method can inhibit boundary aliasing as well as ensuring graphics and image local deformation result smoothness. By applying it to vector graph deformation, multiple complex deformation effects can also be customized.
Bibliographic Details
Provide Feedback
Have ideas for a new metric? Would you like to see something else here?Let us know