RI: Small: Collaborative Research: Structured Inference for Low-Level Vision

RI：小型：协作研究：低级视觉的结构化推理

• 批准号: 1820693
• 负责人: Ayan Chakrabarti
• 金额: $ 15.07万
• 依托单位: Washington University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-11-09 至 2021-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1820693&HistoricalAwards=false
• 关键词: RI; Small; Collaborative; Research; Structured

Vision is a valuable sensing modality because it is versatile. It lets humans navigate through unfamiliar environments, discover assets, grasp and manipulate tools, react to projectiles, track targets through clutter, interpret body language, and recognize familiar objects and people. This versatility stems from low-level visual processes that somehow produce, from ambiguous retinal measurements, useful intermediate representations of depth, surface orientation, motion, and other intrinsic scene properties. This project establishes a mathematical and computational foundation for similar low-level processing in machines. The key challenge it addresses is how to usefully encode and exploit the fact that, visually, the world exhibits substantial intrinsic structure. By advancing understanding of low-level vision in machines, this project makes progress toward computer vision systems that can compare to vision in humans, in terms of accuracy, reliability, speed, and power-efficiency.This research revisits low-level vision, and develops a comprehensive framework that possesses a common abstraction for information from different optical cues; the ability to encode scene structure across large regions and at multiple scales; implementation as parallel and distributed processing; and large-scale end-to-end learnability. The project approaches low-level vision as a structured prediction task, with ambiguous local predictions from many overlapping receptive fields being combined to produce a consistent global scene map that spans the visual field. The structured prediction models are different from those used for categorical tasks such as semantic segmentation, because they are specifically designed to accommodate the distinctive requirements and properties of low-level vision: continuous-valued output spaces; ambiguities that may form equiprobable manifolds; extreme scale variations; and global scene maps with higher-order piecewise smoothness. By strengthening the computational foundations of low-level vision, this project strives to enable many kinds of vision systems that are more efficient and more versatile, and it strives to have impacts across the breadth of computer vision.

视觉是一种有价值的感知方式，因为它具有多功能性。它让人类在陌生的环境中导航，发现资产，掌握和操纵工具，对投射物做出反应，在混乱中跟踪目标，解释肢体语言，并识别熟悉的物体和人。这种多功能性源于低级视觉过程，从模糊的视网膜测量中不知何故产生了有用的深度、表面方向、运动和其他固有场景属性的中间表示。该项目为在机器上进行类似的低级处理奠定了数学和计算基础。它解决的关键挑战是如何有效地编码和利用这样一个事实，即世界在视觉上表现出实质性的内在结构。通过促进对机器低层视觉的理解，该项目朝着在准确性、可靠性、速度和能效方面可以与人类视觉相媲美的计算机视觉系统取得进展。本研究回顾了低层视觉，并开发了一个全面的框架，该框架具有对来自不同光学线索的信息的共同抽象；能够跨大区域和多尺度编码场景结构；实现为并行和分布式处理；以及大规模端到端的可学习能力。该项目将低水平视觉作为一项结构化的预测任务，将来自许多重叠接受野的模棱两可的局部预测结合在一起，以产生横跨视野的一致的全球场景图。结构化预测模型不同于用于语义分割等范畴任务的预测模型，因为它们是专门为适应低水平视觉的独特要求和属性而设计的：连续值输出空间；可能形成等概率流形的歧义；极端尺度变化；以及具有高阶分段平滑的全局场景地图。通过加强低层视觉的计算基础，该项目努力使多种更高效、更通用的视觉系统成为可能，并努力在计算机视觉的广度上产生影响。