NRI-Small: A Biologically Plausible Architecture for Robotic Vision

NRI-Small：一种生物学上合理的机器人视觉架构

• 批准号: 1208522
• 负责人: Nuno Vasconcelos
• 金额: $ 115万
• 依托单位: University of California-San Diego
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2012
• 资助国家: 美国
• 起止时间: 2012-09-01 至 2018-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1208522&HistoricalAwards=false
• 关键词: NRI; Small; Biologically; Plausible; Architecture

The objective of this research is to develop a generic robotic vision architecture that is both biologically plausible and jointly optimal, in a decision theoretic sense, for attention, object tracking, object recognition, and action recognition, in both static and dynamic environments. The research is motivated by the observation that all these problems are solved by biological vision with very homogeneous neural computations. The approach is to exploit a mapping of accepted computational models of visual cortex into the elementary computations of statistical learning and inference in order to derive unified algorithms for all tasks. Intellectual merit: the proposed unification of vision tasks is novel and of paramount importance for robotics, since it is computationally infeasible for a robot to implement a large set of disjoint vision algorithms. It will also exploit task synergies, producing algorithms that leverage the solution of one task to improve performance on another. This will likely enable overall better performance of vision systems. Finally, the project will produce novel insights on the structure of the visual world, and how it can be leveraged by robotic vision, by introducing new models for natural image statistics. Broader impacts: The research has applicability in manufacturing, intelligent systems, health care, homeland security, etc. The expected theoretical insights are likely to be of wide application in statistics (models of feature dependence), neuroscience (models of neural computation), and computer vision (synergistic models). Educationally, the project provides an exciting opportunity for the involvement of undergraduates in research.

本研究的目的是开发一种通用的机器人视觉架构，该架构在生物学上是合理的，在决策理论意义上是共同最优的，用于静态和动态环境中的注意力，物体跟踪，物体识别和动作识别。研究的动机是观察到所有这些问题都是由生物视觉用非常均匀的神经计算来解决的。该方法是将公认的视觉皮层计算模型映射到统计学习和推理的基本计算中，以便为所有任务导出统一的算法。智力优势：提出的视觉任务的统一是新颖的，对机器人来说至关重要，因为机器人实现大量不相交的视觉算法在计算上是不可行的。它还将利用任务协同效应，产生利用一个任务的解决方案来提高另一个任务的性能的算法。这可能会使视觉系统的整体性能更好。最后，该项目将产生关于视觉世界结构的新见解，以及如何通过引入自然图像统计的新模型来利用机器人视觉。更广泛的影响：该研究在制造业、智能系统、医疗保健、国土安全等领域具有适用性。预期的理论见解可能广泛应用于统计学（特征依赖模型），神经科学（神经计算模型）和计算机视觉（协同模型）。在教育方面，该项目为本科生参与研究提供了一个令人兴奋的机会。