2D and 3D level-set algorithms on GPU

G. Tornai, G. Cserey
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引用次数: 7

Abstract

Locating object boundaries, modeling shapes is still an interesting and important task in many applications such as computer vision, object detection, image segmentation and tracking. In this paper we show the implementation of 2D and 3D algorithms based on the level sets using the advantages residing in today's common GPUs. One main goal of this paper is to contribute a development and give one new local-parallel implementation of a fast level set based algorithm via the locally organized processing elements and memory. This algorithm can model and detect any object with arbitrary complex shape and can be applied to situations where no or very few a priori information is available. Our accelerated implementation can handle more initial curves and surfaces which can fuse or merge according to the requirements. This might be a good base to achieve fast and robust detection, segmentation or tracking in medical or autonomous tasks.
基于GPU的2D和3D水平集算法
在计算机视觉、目标检测、图像分割和跟踪等许多应用中,定位目标边界、建模形状仍然是一项有趣而重要的任务。在本文中,我们展示了基于水平集的2D和3D算法的实现,这些算法利用了当今常见gpu的优势。本文的一个主要目标是通过局部组织的处理元素和存储器,为快速水平集算法提供一种新的局部并行实现。该算法可以对任意复杂形状的物体进行建模和检测,适用于没有或很少有先验信息的情况。我们的加速实现可以处理更多的初始曲线和曲面,这些曲面可以根据需要融合或合并。这可能是在医疗或自主任务中实现快速和健壮的检测、分割或跟踪的良好基础。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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