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户外场景中的深度、对比度和基于视图的归位。

Depth, contrast and view-based homing in outdoor scenes.

作者信息

Stürzl Wolfgang, Zeil Jochen

机构信息

ARC Centre of Excellence in Vision Science and Centre for Visual Sciences, Research School of Biological Sciences, The Australian National University, Canberra, ACT, Australia.

出版信息

Biol Cybern. 2007 May;96(5):519-31. doi: 10.1007/s00422-007-0147-3. Epub 2007 Apr 19.

Abstract

Panoramic image differences can be used for view-based homing under natural outdoor conditions, because they increase smoothly with distance from a reference location (Zeil et al., J Opt Soc Am A 20(3):450-469, 2003). The particular shape, slope and depth of such image difference functions (IDFs) recorded at any one place, however, depend on a number of factors that so far have only been qualitatively identified. Here we show how the shape of difference functions depends on the depth structure and the contrast of natural scenes, by quantifying the depth- distribution of different outdoor scenes and by comparing it to the difference functions calculated with differently processed panoramic images, which were recorded at the same locations. We find (1) that IDFs and catchment areas become systematically wider as the average distance of objects increases, (2) that simple image processing operations -- like subtracting the local mean, difference-of-Gaussian filtering and local contrast normalization -- make difference functions robust against changes in illumination and the spurious effects of shadows, and (3) by comparing depth-dependent translational and depth-independent rotational difference functions, we show that IDFs of contrast-normalized snapshots are predominantly determined by the depth-structure and possibly also by occluding contours in a scene. We propose a model for the shape of IDFs as a tool for quantitative comparisons between the shapes of these functions in different scenes.

摘要

全景图像差异可用于自然户外条件下基于视图的归巢,因为它们会随着与参考位置的距离增加而平滑增大(Zeil等人,《美国光学学会会刊A》20(3):450 - 469,2003年)。然而,在任何一个地点记录的这种图像差异函数(IDF)的特定形状、斜率和深度取决于许多因素,到目前为止这些因素仅得到了定性识别。在这里,我们通过量化不同户外场景的深度分布,并将其与在相同位置记录的经过不同处理的全景图像计算出的差异函数进行比较,展示了差异函数的形状如何取决于自然场景的深度结构和对比度。我们发现:(1)随着物体平均距离的增加,IDF和集水区域会系统性地变宽;(2)简单的图像处理操作——如减去局部均值、高斯差分滤波和局部对比度归一化——可使差异函数对光照变化和阴影的虚假效应具有鲁棒性;(3)通过比较深度相关的平移差异函数和深度无关的旋转差异函数,我们表明对比度归一化快照的IDF主要由深度结构以及可能还由场景中的遮挡轮廓决定。我们提出了一个IDF形状模型,作为在不同场景中对这些函数形状进行定量比较的工具。

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