Distance transforms and the saliency maps they induce are widely used in image processing, computer vision, and pattern recognition. The minimum barrier distance (MBD) has proved to provide accurate results in this context. Recently, Géraud et al. have presented a fast-to-compute alternative definition of this distance, called the Dahu pseudo-distance. This distance is efficient, powerful, and have many important applications. However, it is restricted to grayscale images. In this article we revisit this pseudo-distance. First, we offer an extension to multivariate image. We call this extension the vectorial Dahu pseudo-distance . We provide an efficient way to compute it. This new version is not only able to deal with color images but also multi-spectral and multi-modal ones. Besides, through our benchmarks, we demonstrate how robust and competitive the vectorial Dahu pseudo-distance is, compared to other MB-based distances. This shows that this distance is promising for salient object detection, shortest path finding, and object segmentation. Secondly, we combine the Dahu pseudo-distance with the geodesic distance to take into account spatial information from the image. This combination of distances provides efficient results in many applications such as segmentation of thin elements or path finding in images.

距离变换及其引起的显着性图广泛用于图像处理，计算机视觉和模式识别。在这种情况下，最小势垒距离（MBD）已被证明可以提供准确的结果。最近，Géraud等。已经提出了该距离的一种快速计算的替代定义，称为“大湖”伪距。这种距离是有效，强大的，并具有许多重要的应用。但是，它仅限于灰度图像。在本文中，我们将重新研究这种伪距离。首先，我们提供了多元图像的扩展。我们称此扩展为矢量大湖伪距。我们提供了一种有效的方法来进行计算。这个新版本不仅可以处理彩色图像，而且还可以处理多光谱和多模式图像。此外，通过基准测试，我们证明了与其他基于MB的距离相比，矢量大湖伪距的鲁棒性和竞争力。这表明该距离对于显着目标检测，最短路径查找和目标分割很有希望。其次，我们将大湖伪距与测地距离结合起来，以考虑来自图像的空间信息。距离的这种组合在许多应用中提供了有效的结果，例如，薄元素的分割或图像中的路径查找。