Beck Cornelia, Ognibeni Thilo, Neumann Heiko
Institute for Neural Information Processing, University of Ulm, Ulm, Germany.
PLoS One. 2008;3(11):e3807. doi: 10.1371/journal.pone.0003807. Epub 2008 Nov 27.
Optic flow is an important cue for object detection. Humans are able to perceive objects in a scene using only kinetic boundaries, and can perform the task even when other shape cues are not provided. These kinetic boundaries are characterized by the presence of motion discontinuities in a local neighbourhood. In addition, temporal occlusions appear along the boundaries as the object in front covers the background and the objects that are spatially behind it.
METHODOLOGY/PRINCIPAL FINDINGS: From a technical point of view, the detection of motion boundaries for segmentation based on optic flow is a difficult task. This is due to the problem that flow detected along such boundaries is generally not reliable. We propose a model derived from mechanisms found in visual areas V1, MT, and MSTl of human and primate cortex that achieves robust detection along motion boundaries. It includes two separate mechanisms for both the detection of motion discontinuities and of occlusion regions based on how neurons respond to spatial and temporal contrast, respectively. The mechanisms are embedded in a biologically inspired architecture that integrates information of different model components of the visual processing due to feedback connections. In particular, mutual interactions between the detection of motion discontinuities and temporal occlusions allow a considerable improvement of the kinetic boundary detection.
CONCLUSIONS/SIGNIFICANCE: A new model is proposed that uses optic flow cues to detect motion discontinuities and object occlusion. We suggest that by combining these results for motion discontinuities and object occlusion, object segmentation within the model can be improved. This idea could also be applied in other models for object segmentation. In addition, we discuss how this model is related to neurophysiological findings. The model was successfully tested both with artificial and real sequences including self and object motion.
光流是物体检测的重要线索。人类仅通过动态边界就能感知场景中的物体,即使没有提供其他形状线索也能完成该任务。这些动态边界的特征是在局部邻域中存在运动不连续性。此外,当位于前方的物体遮挡背景及其后方空间中的物体时,沿边界会出现时间遮挡。
方法/主要发现:从技术角度来看,基于光流进行分割的运动边界检测是一项艰巨的任务。这是因为沿此类边界检测到的流通常不可靠。我们提出了一种源自人类和灵长类动物皮层视觉区域V1、MT和MSTl中发现的机制的模型,该模型能够在运动边界上实现稳健检测。它包括两种独立的机制,分别基于神经元对空间和时间对比度的响应来检测运动不连续性和遮挡区域。这些机制嵌入在一种受生物启发的架构中,该架构通过反馈连接整合视觉处理不同模型组件的信息。特别是,运动不连续性检测和时间遮挡之间的相互作用使得动态边界检测有了显著改进。
结论/意义:提出了一种利用光流线索检测运动不连续性和物体遮挡的新模型。我们认为,通过结合这些关于运动不连续性和物体遮挡的结果,可以改进模型内的物体分割。这个想法也可以应用于其他物体分割模型。此外,我们讨论了该模型与神经生理学发现的关系。该模型已成功通过包括自我和物体运动的人工序列和真实序列进行测试。