Point‐Pattern Synthesis using Gabor and Random Filters

Abstract

AbstractPoint 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.