Intrinsic Constraints: Local Affine Reconstruction from Multiple Image Signals

尽管视网膜上的图像是二维的，但人类观察者感知到的是三维世界。 如何做到这一点一直是视觉感知中的一个经典问题。研究人员已经分离出一系列“深度线索”，如双眼视差和视网膜速度，这些线索可能会告知视觉系统第三维的信息。已经开发了几个模型来解释第三维的感知如何独立地依赖于每个线索。 然而，当视网膜投射中同时存在多个深度线索时，视觉系统是如何运作的，这一点还有待解释。在美国国家科学基金会的支持下，多米尼博士将测试一种新的深度线索处理模型，该模型与当前的线索组合模型在两个方面有所不同：（i）它计算深度的第三维的相对而非绝对估计，以及（ii）不是根据对从每个线索单独恢复的深度幅度求平均来公式化，其估计是基于对多维线索空间的复杂统计分析。 人类观察者的实验将确定该模型是否准确地捕捉到人类视觉感知的现象，包括人类视觉显示的成功和错误。除了推进我们对视觉感知的基本理解外，这项研究的结果还可能有助于开发更有效的计算机视觉原理，这些原理可用于机器人的设计。

Human observers perceive a three-dimensional world even though images on the retina are two-dimensional. How this can be done has been a classic problem in visual perception. Researchers have isolated a list of "depth cues", such as binocular disparity and retinal velocity, that might inform the visual system about the third dimension. Several models have been developed to explain how perception of the third dimension depends on each cue in isolation. However, what remains to be explained is how the visual system operates when several depth cues are simultaneously available in the retinal projections.With support of the National Science Foundation, Dr. Domini will test a new depth-cue processing model that differs from the current cue-combination model in two respects: (i) it computes relative but not absolute estimates of the third dimension of depth and (ii) rather than being formulated in terms of averaging the depth magnitudes recovered from each cue in isolation, its estimates are based on sophisticated statistical analyses of the multidimensional cue space. Experiments with human observers will determine whether the model accurately captures the phenomena of human visual perception, including both the successes and errors that that human vision shows. In addition to advancing our basic understanding of visual perception, the results of this research may aid the development of more effective computer vision principles that could be used in the design of robots.