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

该项目开发了“扭曲感知”计算机视觉模型和算法，适用于今天的移动相机设备，比如手机。今天的移动相机设备包含非常强大的计算能力，足以考虑执行复杂的计算机视觉问题，如三维深度估计，目标检测和目标识别。然而，由于拍摄时的失真，如低光噪声、模糊、饱和度、曝光过低和压缩等处理伪影，移动相机的功能受到更大的限制。这些扭曲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，也无法找到和识别其中的物体。这个项目创建了具有类似能力的计算机视觉算法，使用新的和新兴的视觉神经科学模型（人们如何看）和三维视觉世界的详细和准确的统计模型（称为自然场景统计模型）。该项目可以影响许多其他相机设备，包括低成本监视和安全摄像机、移动医疗摄像机、在战场条件下运行的军用摄像机等等。本研究开发了使用自然场景统计模型的原则方法，以解决成像条件差的单图像视觉任务问题。具体而言，研究小组研究了用于单图像二维和三维物体和物体类别检测以及协同三维深度估计的鲁棒“扭曲感知”统计图像模型和算法。研究工作包括：(1)开发快速、通用的“野外”目标检测和分类算法，该算法适用于带有数码相机真实伪影的单幅摄影图像；(2)在强大的二维和三维先验自然图像约束的驱动下，设计三维深度估计过程增强的目标和目标类检测机制；(3)利用Reigl VZ-400陆地激光雷达扫描仪对感兴趣的目标类别构建一个新的彩色+3D注释数据库，生成比现有数据集更高的分辨率和丰富度的数据，并完成图像标签和边界框目标位置的手工注释。这个数据库一旦可用，就对整个社区免费开放。

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.