dc.contributor.author | Huang, Xingchang | en_US |
dc.contributor.author | Memari, Pooran | en_US |
dc.contributor.author | Seidel, Hans-Peter | en_US |
dc.contributor.author | Singh, Gurprit | en_US |
dc.contributor.editor | Ghosh, Abhijeet | en_US |
dc.contributor.editor | Wei, Li-Yi | en_US |
dc.date.accessioned | 2022-07-01T15:37:21Z | |
dc.date.available | 2022-07-01T15:37:21Z | |
dc.date.issued | 2022 | |
dc.identifier.issn | 1467-8659 | |
dc.identifier.uri | https://doi.org/10.1111/cgf.14596 | |
dc.identifier.uri | https://diglib.eg.org:443/handle/10.1111/cgf14596 | |
dc.description.abstract | Point pattern synthesis requires capturing both local and non-local correlations from a given exemplar. Recent works employ deep hierarchical representations from VGG-19 [SZ15] convolutional network to capture the features for both point-pattern and texture synthesis. In this work, we develop a simplified optimization pipeline that uses more traditional Gabor transform-based features. These features when convolved with simple random filters gives highly expressive feature maps. The resulting framework requires significantly less feature maps compared to VGG-19-based methods [TLH19; RGF*20], better captures both the local and non-local structures, does not require any specific data set training and can easily extend to handle multi-class and multi-attribute point patterns, e.g., disk and other element distributions. To validate our pipeline, we perform qualitative and quantitative analysis on a large variety of point patterns to demonstrate the effectiveness of our approach. Finally, to better understand the impact of random filters, we include a spectral analysis using filters with different frequency bandwidths. | en_US |
dc.publisher | The Eurographics Association and John Wiley & Sons Ltd. | en_US |
dc.rights | Attribution 4.0 International License | |
dc.rights.uri | https://creativecommons.org/licenses/by/4.0/ | |
dc.subject | CCS Concepts: Computing methodologies --> Point pattern synthesis; Point-based texture synthesis | |
dc.subject | Computing methodologies | |
dc.subject | Point pattern synthesis | |
dc.subject | Point based texture synthesis | |
dc.title | Point-Pattern Synthesis using Gabor and Random Filters | en_US |
dc.description.seriesinformation | Computer Graphics Forum | |
dc.description.sectionheaders | Patterns and Noises | |
dc.description.volume | 41 | |
dc.description.number | 4 | |
dc.identifier.doi | 10.1111/cgf.14596 | |
dc.identifier.pages | 169-179 | |
dc.identifier.pages | 11 pages | |