RI: Small: Collaborative Research: Contour-Assisted Visual Inference: Systems, Algorithms, and Applications

RI：小型：协作研究：轮廓辅助视觉推理：系统、算法和应用

• 批准号: 1218177
• 负责人: Jingyi Yu
• 金额: $ 20.44万
• 依托单位: University of Delaware
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2012
• 资助国家: 美国
• 起止时间: 2012-09-01 至 2015-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1218177&HistoricalAwards=false
• 关键词: RI; Small; Collaborative; Research; Contour

Occlusion contour (OC) is well known to play important roles in many computer vision tasks. Unlike regular photographs, an OC image removes the effects of illumination, texture, and appearance while preserving important depth edges and silhouette. This project develops a comprehensive framework for acquiring, processing, and utilizing OCs in visual inference tasks. On the sensor front, the research team develops a new Occlusion Contour Camera or OC-Cam. The new OC-Cam extends the multi-flash camera by coupling an array of controllable infrared (IR) LEDs and a visible-IR camera pair. On the algorithm and application fronts, the research team systematically develops OC-assisted visual inference algorithms. For recognition, the acquired OCs are used as a feature filter to improve category-level object recognition. For tracking, the PIs apply OCs to enhance target representation by filtering out the background and texture edges. Furthermore, the research team investigates the previously under-explored problems of OC-assisted image summarization and privacy protection.This project can cast deep impact on broad areas of computer vision, artificial intelligence, criminal justices, and robotics, both in research and education. Due to the importance of OCs in human vision, the results can produce a testbed for the study of visual psychology. Furthermore, the OC-Cam is expected to serve as conceptual inspiration for constructing the next-generation surveillance systems. Finally, the captured OC datasets and relevant tools are made available to other researchers, to provide a platform for validating new OC-based computer vision algorithms.

遮挡轮廓线（OC）在许多计算机视觉任务中起着重要的作用。与常规照片不同，OC图像去除了照明，纹理和外观的影响，同时保留了重要的深度边缘和轮廓。这个项目开发了一个全面的框架，用于在视觉推理任务中获取，处理和利用OC。在传感器方面，研究团队开发了一种新的遮挡轮廓相机或OC-Cam。新的OC-Cam通过耦合可控红外（IR）LED阵列和可见光红外相机对来扩展多闪光灯相机。 在算法和应用方面，研究团队系统地开发了OC辅助的视觉推理算法。对于识别，所获得的OC被用作特征过滤器，以提高类别级对象识别。对于跟踪，PI应用OC通过过滤掉背景和纹理边缘来增强目标表示。此外，研究团队还对OC辅助图像摘要和隐私保护等以前未被充分探索的问题进行了研究，该项目将对计算机视觉、人工智能、刑事司法和机器人等广泛领域的研究和教育产生深远影响。由于OC在人类视觉中的重要性，该结果可以为视觉心理学研究提供测试平台。此外，OC-Cam有望成为构建下一代监控系统的概念灵感。最后，捕获的OC数据集和相关工具可供其他研究人员使用，为验证新的基于OC的计算机视觉算法提供平台。