Xie Yiran, Liu Nianjun, Barnes Nick
College of Engineering and Computer Science of Australian National University and Canberra Research Laboratory of National ICT Australia.
Annu Int Conf IEEE Eng Med Biol Soc. 2012;2012:5314-8. doi: 10.1109/EMBC.2012.6347194.
This paper presents a novel low-resolution phosphene visualization of depth and boundary computed by a two-layer Associative Markov Random Fields. Unlike conventional methods modeling the depth and boundary as an individual MRF respectively, our algorithm proposed a two-layer associative MRFs framework by combining the depth with geometry-based surface boundary estimation, in which both variables are inferred globally and simultaneously. With surface boundary integration, the experiments demonstrates three significant improvements as: 1) eliminating depth ambiguities and increasing the accuracy, 2) providing comprehensive information of depth and boundary for human navigation under low-resolution phosphene vision, 3) when integrating the boundary clues into downsampling process, the foreground obstacle has been clearly enhanced and discriminated from the surrounding background. In order to gain higher efficiency and lower computational cost, the work is initialized on segmentation based depth plane fitting and labeling, and then applying the latest projected graph cut for global optimization. The proposed approach has been tested on both Middlebury and indoor real-scene data set, and achieves a much better performance with significant accuracy than other popular methods in both regular and low resolutions.
本文提出了一种新颖的低分辨率光幻视可视化方法，用于呈现由两层关联马尔可夫随机场计算出的深度和边界。与传统方法分别将深度和边界建模为单独的马尔可夫随机场不同，我们提出的算法通过将深度与基于几何的表面边界估计相结合，构建了一个两层关联马尔可夫随机场框架，其中两个变量可以全局且同时推断。通过表面边界整合，实验证明了三个显著改进：1）消除深度模糊性并提高准确性；2）在低分辨率光幻视视觉下为人类导航提供深度和边界的全面信息；3）当将边界线索整合到下采样过程中时，前景障碍物得到明显增强并与周围背景区分开来。为了获得更高的效率和更低的计算成本，该工作在基于分割的深度平面拟合和标记上进行初始化，然后应用最新的投影图割进行全局优化。所提出的方法已在米德尔伯里数据集和室内真实场景数据集上进行测试，并且在常规分辨率和低分辨率下均比其他流行方法具有更高的准确性和更好的性能。
