Morphological grayscale reconstruction in image analysis: Applications and efficient algorithms
Morphological grayscale reconstruction in image analysis: Applications and efficient algorithms
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DOI：
10.1109/83.217222
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发表时间：
1993-04-01
影响因子：
10.6
通讯作者：
Vincent, Luc
中科院分区：
文献类型：
--
作者：
Vincent, Luc
形态学重建是一组通常被称为测地线的图像算子的一部分。在二进制的情况下，重建简单地提取二进制图像I（掩模）的连接分量，其由包含在I中的（二进制）图像J“标记”。这种转换可以扩展到灰度的情况下，它被证明是非常有用的几个图像分析任务。本文首先给出了灰度重建的两种不同的形式化定义。然后，它说明了使用灰度重建在各种图像处理应用程序，并旨在证明这种变换的图像滤波和分割任务的有用性。最后，本文着重于实现问题：标准的并行和顺序重建方法简要回顾，其共同的缺点是他们的效率低下，在传统的计算机上。为了改善这种情况，介绍了一种新的算法，它是基于区域最大值的概念，并利用宽度优先的图像扫描通过一个队列的像素。它与顺序技术的组合导致混合灰度重建算法，这是一个数量级比任何先前已知的算法更快。
Morphological reconstruction is part of a set of image operators often referred to as geodesic. In the binary case, reconstruction simply extracts the connected components of binary image I (the mask) which are "marked" by a (binary) image J contained in I. This transformation can be extended to the grayscale case, where it turns out to be extremely useful for several image analysis tasks. This paper first provides two different formal definitions of grayscale reconstruction. It then illustrates the use of grayscale reconstruction in various image processing applications and aims at demonstrating the usefulness of this transformation for image filtering and segmentation tasks. Lastly, the paper focuses on implementation issues: The standard parallel and sequential approaches to reconstruction are briefly recalled; their common drawback is their inefficiency on conventional computers. To improve this situation, a new algorithm is introduced, which is based on the notion of regional maxima and makes use of breadthfirst image scannings implemented via a queue of pixels. Its combination with the sequential technique results in a hybrid grayscale reconstruction algorithm which is an order of magnitude faster than any previously known algorithm.