The Postdoctoral Station at Xihua University Based on Collaboration Innovation Center of Sichuan Automotive Key Parts, Xihua University, Chengdu 610039, China.
School of Control Engineering, Chengdu University of Information Technology, Chengdu 610225, China.
Molecules. 2019 Mar 29;24(7):1235. doi: 10.3390/molecules24071235.
Image edge detection is a fundamental problem in image processing and computer vision, particularly in the area of feature extraction. However, the time complexity increases squarely with the increase of image resolution in conventional serial computing mode. This results in being unbearably time consuming when dealing with a large amount of image data. In this paper, a novel resolution free parallel implementation algorithm for gradient based edge detection, namely EDENP, is proposed. The key point of our method is the introduction of an enzymatic numerical P system (ENPS) to design the parallel computing algorithm for image processing for the first time. The proposed algorithm is based on a cell-like P system with a nested membrane structure containing four membranes. The start and stop of the system is controlled by the variables in the skin membrane. The calculation of edge detection is performed in the inner three membranes in a parallel way. The performance and efficiency of this algorithm are evaluated on the CUDA platform. The main advantage of EDENP is that the time complexity of O ( 1 ) can be achieved regardless of image resolution theoretically.
图像边缘检测是图像处理和计算机视觉中的一个基本问题，特别是在特征提取领域。然而，在传统的串行计算模式中，时间复杂度与图像分辨率的增加成正比。这导致在处理大量图像数据时，时间消耗变得难以忍受。在本文中，我们提出了一种基于梯度的边缘检测的无分辨率并行实现算法，即 EDENP。我们方法的关键是首次引入一种酶数值 P 系统（ENPS）来设计图像处理的并行计算算法。所提出的算法基于具有嵌套膜结构的细胞样 P 系统，该结构包含四个膜。系统的开始和停止由皮肤膜中的变量控制。边缘检测的计算在内部的三个膜中以并行的方式进行。该算法的性能和效率在 CUDA 平台上进行了评估。EDENP 的主要优点是，理论上可以实现时间复杂度为 O(1)，而与图像分辨率无关。
