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.

这项研究的目的是开发一种通用的机器人视觉体系结构，在静态和动态环境中，在决策理论意义上既有生物学上的合理又具有最佳的最佳方式，以供注意力，对象跟踪，对象识别和动作识别。这项研究是由观察到的，即通过非常均匀的神经计算来解决所有这些问题。该方法是将视觉皮层的可接受计算模型映射到统计学习和推理的基本计算中，以得出所有任务的统一算法。智力优点：拟议的视觉任务统一是新颖的，对于机器人来说至关重要，因为机器人在计算上实现了一系列脱节视觉算法是不可行的。它还将利用任务协同作用，生成利用一个任务解决方案的算法来提高另一个任务的性能。这可能会使视力系统的总体性能更好。最后，该项目将通过引入新的自然图像统计模型来对视觉世界的结构以及如何通过机器人视觉来利用它。更广泛的影响：该研究在制造，智能系统，医疗保健，国土安全等方面具有适用性。预期的理论见解可能在统计数据（功能依赖模型），神经科学（神经计算模型）和计算机视觉（协同模型）中具有广泛的应用。在教育上，该项目为大学生参与研究提供了一个令人兴奋的机会。