Cartoon filter via adaptive abstraction
Journal of Visual Communication and Image Representation, ISSN: 1047-3203, Vol: 36, Page: 149-158
2016
- 15Citations
- 14Captures
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.
Article Description
Abstraction in computer graphics defines a procedure that discriminates the essential information that is worth keeping. Usually details, that correspond to higher frequency components, allow to distinguish otherwise similar images. Vice versa, low frequencies are related to the main information, which are larger structures. Contours themselves may also be identified by high frequencies and separate each pictured component. The underlying idea of the proposed algorithm consists in identifying these edges, by a redundant wavelet transform, and in blurring the inner areas of the components, by an adaptive circular median filter. In spite of its implementation simplicity, our unsupervised methodology provides results similar to those obtained by more complex techniques already described in the literature.
Bibliographic Details
http://www.sciencedirect.com/science/article/pii/S1047320316000213; http://dx.doi.org/10.1016/j.jvcir.2016.01.012; http://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=84958176359&origin=inward; https://linkinghub.elsevier.com/retrieve/pii/S1047320316000213; https://api.elsevier.com/content/article/PII:S1047320316000213?httpAccept=text/xml; https://api.elsevier.com/content/article/PII:S1047320316000213?httpAccept=text/plain; https://dx.doi.org/10.1016/j.jvcir.2016.01.012
Elsevier BV
Provide Feedback
Have ideas for a new metric? Would you like to see something else here?Let us know