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: 1218156
• 负责人: Haibin Ling
• 金额: $ 24.89万
• 依托单位: Temple 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: 2012-09-01 至 2016-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1218156&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.

闭塞轮廓（OC）众所周知，在许多计算机视觉任务中都起着重要作用。与常规照片不同，OC图像消除了照明，纹理和外观的影响，同时保留了重要的深度边缘和轮廓。该项目开发了一个综合框架，用于在视觉推理任务中获取，处理和利用OC。在传感器方面，研究团队开发了一个新的闭塞轮廓摄像头或OC-CAM。新的OC-CAM通过耦合一系列可控红外（IR）LED和可见的IR相机对来扩展多闪存摄像机。 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。为了识别，获得的OCs用作功能过滤器，以改善类别级的对象识别。对于跟踪，PIS应用OC来通过滤除背景和纹理边缘来增强目标表示。此外，研究小组还研究了先前在OC辅助图像摘要和隐私保护方面爆炸不足的问题。该项目可以对计算机视觉，人工智能，犯罪法官和教育方面的广泛领域产生深远影响。由于OC在人类视力中的重要性，结果可以为视觉心理学研究产生测试床。此外，预计OC-CAM将作为构建下一代监视系统的概念灵感。最后，捕获的OC数据集和相关工具可供其他研究人员使用，以提供一个验证新的基于OC的计算机视觉算法的平台。