CAREER: Making Computer Vision Successful in Scattering Media

职业：使计算机视觉在散射介质中取得成功PI：Srinivasa Narasimhan卡内基梅隆大学近年来，计算机视觉在图像传感和解释的核心领域取得了重大进展。这一成功导致了从智能交通和安全到海洋学（水下成像），天文学（望远镜和卫星成像），甚至生物学和医学系统（显微镜和医学成像）等应用领域对视觉技术的巨大需求。 不幸的是，有一个基本的障碍可以阻止视觉在这些领域取得成功的影响-假设光在透明介质（纯空气）中传播而没有任何改变。因此，今天的视觉系统无法在存在各种颗粒介质的光散射的情况下执行，例如恶劣天气（雾，薄雾，霾，雪，雨），浑浊的水，烟雾，灰尘，烟雾和生物组织。本研究致力于使计算机视觉在散射介质中取得成功。在计算机视觉中，图像形成被定义为“从3D世界到2D图像的几何映射”，这固有地导致信息的丢失。PI强烈认为，光散射不能被视为传统视觉算法需要克服的“噪声”，而是一种新的光“编码”形式，因此，图像本身。 然后，关键的想法是推导出一系列紧凑的基于物理的光传输的解析（或半解析）模型来表示散射介质中的成像。这些模型将“丢失的第三维”编码回图像中。模型的分析形式-虽然不像计算物理学中的缓慢模拟那样复杂-足够精确，可以模拟图像中的聚集散射效应，从而使反转光传输成为可能。逆向光传输方法将与传统的视觉算法结合应用，以匹配它们在晴朗空气中的性能。这项研究的结果将在各种领域产生广泛而长期的影响。帮助驾驶员导航的（半）自动智能交通系统将能够在雾、雪和雨等常见的恶劣天气条件下运行，实际上是在最需要的时候。同样，现场机器人将在烟雾和灰尘等危险环境中更好地导航。水下探测、安全和救援任务可以在黑暗的水下条件下进行。了解组织的光学特性可以帮助医生对肿瘤和癌症进行医学诊断。最后，导出的模型还可用于为数字娱乐（电影和视频游戏）、科学教育和培训的图像添加逼真的散射效果。URL：http://www.cs.cmu.edu/~srinivas/CAREER/

CAREER: Making Computer Vision Successful in Scattering MediaPI: Srinivasa NarasimhanCarnegie Mellon UniversityIn recent years, computer vision has seen significant advances in the core areas of image sensing and interpretation. This success has resulted in great demand for vision techniques in application domains ranging from intelligent transportation and security to oceanography (underwater imaging), to astronomy (telescope and satellite imaging), to even biology and medical systems (microscopic and medical imaging). Unfortunately, there is one fundamental hurdle that can stop vision from having successful impact in these areas --- the assumption that light propagates in a transparent medium (pure air) without any alteration. Thus, today vision systems fail to perform in the presence of light scattering by a wide range of particulate media, such as bad weather (fog, mist, haze, snow, rain), murky water, smoke, dust, smog and biological tissue.This research is devoted to making computer vision successful in scattering media. In computer vision, image formation has been defined as "a geometric mapping from the 3D world to the 2D image", which inherently leads to loss of information. The PI strongly argues that light scattering must not be viewed as "noise" that a traditional vision algorithm needs to overcome, but rather as a new form of "encoding" of light and hence, the images themselves. The key idea then is to derive a series of compact physically based analytic (or semi-analytic) models for light transport to represent image formation in scattering media. These models encode the "lost third dimension" back into images. The analytic forms of the models--- though not as elaborate as the slow simulations in computational physics --- are accurate enough to model the aggregate scattering effects in images and thus make it possible to invert light transport. The inverse light transport methods will then be applied in conjunction with traditional vision algorithms to match their performances in clear air.The results from this research will have broad and long-term impact across a wide variety of domains. The (semi-)automatic intelligent transportation systems that assist drivers in navigation will be able operate in common bad weather conditions such as fog, snow and rain, indeed when they are most required. Similarly, field robots will navigate better in hazardous environments such as smoke and dust. Underwater exploration, safety, and rescue tasks can be made possible in murky underwater conditions.Understanding optical properties of tissues can assist doctors in medical diagnosis of tumors and cancers. Finally, the derived models can be used to also add realistic effects of scattering to imagery for digital entertainment (movies and video games), scientific education and training.URL: http://www.cs.cmu.edu/~srinivas/CAREER/