Show HN: Blue Noise – Interactive Explanation of Void and Cluster Algorithm
2 by laszlokorte | 0 comments on Hacker News.
After reading about the generation of blue noise here on HN a few times my goal was to implement my own variant of the the Cluster and Void algorithm in the most straight-foward way possible, while also visualizing each step. (JavaScript is required in order to step through the algorithm) Most other Blue Noise generator implementations are optimized for speed. Many explanations of the Cluster and Void algorithm I found online were overly complicated or focusing on details that do no help the initial understanding. My implementation is optimized for readability and understanding. I find it very inspiring see an algorithm broken down to its most essential steps. For one in order to better understand the algorithm itself but also for transfering its key concepts to other tasks, for example when designing my own algorithms. Eg in my rather high level python/numpy implementation one can easily understand that the two phases of the algorithm (phase 2 and phase 3) have no data dependency between each other and can therefor be parallelized. Additionally the numpy implementation demonstrates how the application of high level concepts like rank-polymorphism and convolution allow to express a sophisticated algorithm in only a few lines of code. Hope you like it.
2 by laszlokorte | 0 comments on Hacker News.
After reading about the generation of blue noise here on HN a few times my goal was to implement my own variant of the the Cluster and Void algorithm in the most straight-foward way possible, while also visualizing each step. (JavaScript is required in order to step through the algorithm) Most other Blue Noise generator implementations are optimized for speed. Many explanations of the Cluster and Void algorithm I found online were overly complicated or focusing on details that do no help the initial understanding. My implementation is optimized for readability and understanding. I find it very inspiring see an algorithm broken down to its most essential steps. For one in order to better understand the algorithm itself but also for transfering its key concepts to other tasks, for example when designing my own algorithms. Eg in my rather high level python/numpy implementation one can easily understand that the two phases of the algorithm (phase 2 and phase 3) have no data dependency between each other and can therefor be parallelized. Additionally the numpy implementation demonstrates how the application of high level concepts like rank-polymorphism and convolution allow to express a sophisticated algorithm in only a few lines of code. Hope you like it.