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应方式，因为它具有多功能性。它使人类可以在陌生的环境中导航，发现资产，掌握和操纵工具，对弹丸做出反应，通过混乱跟踪目标，解释肢体语言并识别熟悉的对象和人。这种多功能性源于低水平的视觉过程，这些视觉过程以模棱两可的视网膜测量，深度的有用中间表示，表面取向，运动和其他内在场景特性。该项目为机器中类似的低级处理建立了数学和计算基础。它解决的主要挑战是如何有效地编码和利用这样一个事实，即世界上具有实质性的内在结构。通过促进对机器中低水平视觉的了解，该项目在计算机视觉系统方面取得了进步，可以从准确性，可靠性，速度和发电效率方面与人类的视觉进行比较。这项研究重新审视了低级视觉，并开发了一个全面的框架，该框架具有来自不同光学线索的信息的共同抽象；在大区域和多个尺度上编码场景结构的能力；实施作为并行和分布式处理；和大规模的端到端可学习性。该项目将低级视觉作为一项结构化的预测任务，并结合了许多重叠的接收场的局部预测，以产生跨越视野的一致的全局场景图。结构化的预测模型与用于分类任务（例如语义分割）的模型不同，因为它们是专门设计的，旨在适应低级视觉的独特要求和属性：连续价值的输出空间；可能形成均衡流形的歧义；极端尺度变化；和全球场景映射具有高阶分段平滑度。通过加强低级视觉的计算基础，该项目致力于实现许多更有效，更通用的视觉系统，并努力在计算机视觉广度上产生影响。