Gazagnes Simon, Wilkinson Michael H F
IEEE Trans Image Process. 2021;30:3664-3675. doi: 10.1109/TIP.2021.3064223. Epub 2021 Mar 17.
Connected filters and multi-scale tools are region-based operators acting on the connected components of an image. Component trees are image representations to efficiently perform these operations as they represent the inclusion relationship of the connected components hierarchically. This paper presents disccofan (DIStributed Connected COmponent Filtering and ANalysis), a new method that extends the previous 2D implementation of the Distributed Component Forests (DCFs) to handle 3D processing and higher dynamic range data sets. disccofan combines shared and distributed memory techniques to efficiently compute component trees, user-defined attributes filters, and multi-scale analysis. Compared to similar methods, disccofan is faster and scales better on low and moderate dynamic range images, and is the only method with a speed-up larger than 1 on a realistic, astronomical floating-point data set. It achieves a speed-up of 11.20 using 48 processes to compute the DCF of a 162 Gigapixels, single-precision floating-point 3D data set, while reducing the memory used by a factor of 22. This approach is suitable to perform attribute filtering and multi-scale analysis on very large 2D and 3D data sets, up to single-precision floating-point value.
连通滤波器和多尺度工具是基于区域的算子，作用于图像的连通分量。组件树是一种图像表示形式，能够有效地执行这些操作，因为它们以层次结构表示连通分量的包含关系。本文提出了disccofan（分布式连通分量滤波与分析），这是一种新方法，它扩展了之前分布式组件森林（DCF）的二维实现，以处理三维处理和更高动态范围的数据集。disccofan结合了共享内存和分布式内存技术，以有效地计算组件树、用户定义的属性滤波器和多尺度分析。与类似方法相比，disccofan在低动态范围和中等动态范围图像上速度更快且扩展性更好，并且是唯一在实际的天文浮点数据集上加速比大于1的方法。使用48个进程计算一个1620亿像素的单精度浮点三维数据集的DCF时，它实现了11.20的加速比，同时将内存使用量减少了22倍。这种方法适用于对非常大的二维和三维数据集执行属性滤波和多尺度分析，直至单精度浮点值。
