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RI: Large: Collaborative Research: Reconstructive recognition: Uniting statistical scene understanding and physics-based visual reasoning

RI：大型：协作研究：重建识别：结合统计场景理解和基于物理的视觉推理

基本信息

批准号：
1212928
负责人：
Todd Zickler
金额：
$ 109.09万
依托单位：
Harvard University
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：
2012-10-01 至 2018-09-30
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1212928&HistoricalAwards=false
关键词：
RI Large Collaborative Research Reconstructive

项目摘要

This project is creating a novel paradigm for computer vision, termed "reconstructive recognition", that incorporates the strongest elements of previous machine learning-based recognition efforts and the strongest elements of previous reconstruction efforts based on radiometric reasoning. The goal is to provide a new foundation for machine perception, and the potential for a transformative advance in applications of computer vision. The project seeks novel physics-based methods for recognition as well as novel learning-based methods for interpreting pixel values in terms of the physics of a scene. The agenda is structured around four aims: Aim I develops generalized reconstructive processes that unify the recovery of shape, materials, motion and illumination. Aim II focuses on supervised visual learning methods that exploit such reconstructive image representations. Aim III pursues unsupervised discovery of reconstructive representations that converge to be similar to the engineered models of Aim I. Finally, Aim IV introduces well-defined challenge problems that focus the field and serve as measurable proxies for progress in computer vision applications that have high potential impact on society. There is a significant broader impact to this project, not least being the improvement in computer vision pedagogy that ensues from a reunification of the currently divergent recognition and reconstruction views of the field. More broadly, this project pursues critical steps toward a future where machines can see, a future that will bring changes to robotics, human-computer interfaces, security, and autonomous navigation, to name a few.

该项目正在为计算机视觉创建一个新的范例，称为“重建识别”，它结合了以前基于机器学习的识别工作的最强元素和以前基于辐射推理的重建工作的最强元素。其目标是为机器感知提供新的基础，并为计算机视觉应用的变革性进步提供潜力。该项目寻求新的基于物理的识别方法，以及新的基于学习的方法，用于根据场景的物理特性解释像素值。议程围绕四个目标：目标I开发统一形状，材料，运动和照明恢复的广义重建过程。目标II侧重于利用这种重建图像表示的监督视觉学习方法。Aim III追求无监督地发现重构表示，这些重构表示收敛到与Aim I的工程模型相似。最后，Aim IV介绍了明确定义的挑战问题，这些问题集中在该领域，并作为对社会具有高度潜在影响的计算机视觉应用进展的可衡量指标。这个项目有一个显着的更广泛的影响，尤其是计算机视觉教学法的改进，从目前不同的识别和重建该领域的观点的统一。更广泛地说，这个项目追求的是机器可以看到的未来的关键步骤，这将给机器人、人机界面、安全和自主导航带来变化。