Centre for Vision, Speech and Signal Processing, University of Surrey, Guildford, UK.
PLoS One. 2010 Jun 9;5(6):e10663. doi: 10.1371/journal.pone.0010663.
In its early stages, the visual system suffers from a lot of ambiguity and noise that severely limits the performance of early vision algorithms. This article presents feedback mechanisms between early visual processes, such as perceptual grouping, stereopsis and depth reconstruction, that allow the system to reduce this ambiguity and improve early representation of visual information. In the first part, the article proposes a local perceptual grouping algorithm that - in addition to commonly used geometric information - makes use of a novel multi-modal measure between local edge/line features. The grouping information is then used to: 1) disambiguate stereopsis by enforcing that stereo matches preserve groups; and 2) correct the reconstruction error due to the image pixel sampling using a linear interpolation over the groups. The integration of mutual feedback between early vision processes is shown to reduce considerably ambiguity and noise without the need for global constraints.
在早期阶段，视觉系统受到大量的模糊性和噪声的影响，这严重限制了早期视觉算法的性能。本文提出了早期视觉过程之间的反馈机制，例如知觉分组、立体视和深度重建，这些机制允许系统减少这种模糊性并改善视觉信息的早期表示。在第一部分，本文提出了一种局部知觉分组算法，该算法除了常用的几何信息外，还利用了局部边缘/线特征之间的一种新颖的多模态度量。然后，分组信息用于：1）通过强制立体匹配保持分组来消除立体视的歧义；2）使用组上的线性插值来纠正由于图像像素采样而导致的重建误差。早期视觉过程之间的相互反馈的集成被证明可以在不需要全局约束的情况下大大减少模糊性和噪声。
