RI: Small: Tasking on Natural Image Statistics: 2D and 3D Object and Category Detection in the Wild

该项目开发“失真感知”计算机视觉模型和算法，适用于当今的移动的相机设备，如手机中的相机设备。当今的移动的相机设备包含非常强大的计算能力，实际上足以考虑执行复杂的计算机视觉问题，诸如三维深度估计、对象检测和对象识别。然而，移动的相机能力由于捕获时的失真（诸如低光噪声、模糊、饱和度、曝光过度/不足以及诸如压缩的处理伪影）而受到更多限制。这些失真导致大多数计算机视觉算法“崩溃”，使它们无法准确地重建3D世界或找到并识别其中的对象。本项目使用视觉神经科学的新兴模型（人们如何观看）和三维视觉世界的详细准确的统计模型（称为自然场景统计模型）创建具有类似功能的计算机视觉算法。该项目可能会影响许多其他相机设备，包括低成本的监控和安全摄像机，移动的医疗摄像机，在战场条件下运行的军用摄像机，以及更多。这项研究开发了原则性的方法，使用自然场景统计模型，以解决困难的单图像视觉任务的问题，在恶劣的成像条件下。 具体来说，研究小组研究了用于单图像2D和3D对象和对象类别检测以及协同3D深度估计的鲁棒的“失真感知”统计图像模型和算法。研究工作包括：（1）开发用于快速、通用的目标检测和分类的算法，该算法在遭受来自数码相机的真实伪影的单个摄影图像上操作;（2）设计由3D深度估计过程增强的目标和目标类别检测机制，由强大的2D和3D先验自然图像约束驱动;以及（3）使用Reigl VZ-400地面激光雷达扫描仪对感兴趣的对象类别构建HD精确校准的RGBD数据的新的注释的Color+3D数据库，产生比现有数据集更高分辨率和丰富度的数据，完成图像标签以及边界框对象位置的手动注释。该数据库一旦可用，将免费向整个社区开放。

This project develops "distortion-aware" computer vision models and algorithms suitable for today's mobile camera devices, such as are found in cell phones. Today's mobile camera devices contain remarkably powerful computing capability, sufficient, in fact to contemplate performing sophisticated computer vision problems such as three-dimensional depth estimation, object detection and object recognition. However, mobile camera capabilities are much more limited due to distortions on capture, such as low-light noise, blur, saturation, over/under exposure, and processing artifacts such as compression. These distortions cause most computer vision algorithms to "break," making them unable to accurately recreate the 3D world or to find and recognize objects in it. This project creates computer vision algorithms with similar capability, using new and emerging models of visual neuroscience (how people see) and detailed and accurate statistical models of the three dimensional visual world (called natural scene statistic models). The project can impact many other camera devices, including low-cost surveillance and security cameras, mobile medical cameras, military cameras operating under battlefield conditions, and more.This research develops principled approaches to using natural scene statistics models to solve difficult single-image visual tasking problems under poor imaging conditions. Specifically, the research team studies robust 'distortion-aware' statistical image models and algorithms for single-image 2D and 3D object and object category detection and synergistic 3D depth estimation. The research work includes (1) developing algorithms for fast, generic object detection and categorization "in-the-wild" that operate on single photographic images suffering authentic artifacts from digital cameras; (2) designing object and object class detection mechanisms augmented by 3D depth estimation processes, driven by powerful 2D and 3D prior natural image constraints; and (3) constructing a new annotated Color+3D database of HD precision-calibrated RGBD data using a Reigl VZ-400 Terrestrial Lidar Scanner on object categories of interest, yielding data of higher resolution and richness than existing datasets, complete with image labels as well as hand annotations of bounding-box object locations. This database is free to the community at large once it is available.