Bringing Images to Light

很长一段时间以来，实际的计算机视觉应用一直局限于“机器视觉”领域：一种用于装配线上的自动检测和分析的基于图像的技术。近年来，随着廉价数码相机的出现，我们见证了计算机视觉应用在大规模消费应用中的出现。例如，“智能”摄像头在人微笑时自动检测人脸并拍摄，搜索引擎成功地从文本查询中检索到图像，在线数字地图上增加了街道级别的图像，增强现实应用程序通常在智能手机上可用。尽管取得了所有这些进展，但这只是个开始。计算机视觉研究界正在积极开展更具挑战性的任务，例如图像的全自动理解，在不远的将来，这肯定会找到实现商业进步的途径。 尽管取得了这些进展，但计算机视觉系统仍面临着一个主要限制：它们难以适应剧烈变化的光照条件，尤其是室外。特别是，太阳以其令人眼花缭乱的强度创造了一些非常难以处理的效果，如高光和投射阴影。虽然人眼可以通过数百万年的适应很容易地适应这些变化，但数码相机和对其图像进行操作的算法就不能说同样的话了。 这项研究计划将专注于允许计算机视觉算法适应甚至利用图像中的照明条件的问题。我们将开发关于照明和场景组件的联合推理可以帮助我们更好地理解图像的新范式。为此，我们将实现以下四个目标。我们将：1)引入新的推理算法，用于联合推理人脸外观和光照条件，以改进在具有挑战性的光照条件下的人脸检测；2)开发新的光照模型，以数据驱动的方式捕捉对象(人和汽车)的外观，以构建更好的对象检测器；3)结合外观的物理模型，更好地将图像分割为语义区域；以及4)提供通用框架，以理解光照是计算机视觉中的有用线索，即使在具有挑战性的消费者照片的世界中也是如此。 除了上述应用外，本研究计划中提出的活动还将影响其他领域，如计算机图形学、智能交通和机器人技术，这些领域都与加拿大有关。在计算机图形学中，有关图像中照明条件的附加信息有助于改进应用程序，例如预可视化、数字资产捕获和管理以及图像编辑。智能交通系统的安全功能，如自动驾驶汽车，通常依赖于摄像头来检测道路上的障碍物，这在很大程度上取决于它们适应不同照明条件的能力。同样，通过开发更好的户外照明模型，户外探索机器人也将从这项研究中受益。拉瓦尔大学创建这一研究项目将通过其关键的技术贡献，并通过培训高素质的开发人员，帮助保持甚至扩大加拿大在这些领域的相关性。

Practical computer vision applications have, for a long time, found themselves confined to the realm of "machine vision": the image-based technology for automatic inspection and analysis used on assembly lines. In recent years, and in combination with the advent of cheap digital cameras, we have witnessed an emergence of computer vision applications in massive-scale consumer applications. For example, "intelligent" cameras automatically detect faces and shoot when the person is smiling, search engines successfully retrieve images from a text query, online digital maps are augmented with street-level imagery, and augmented reality applications are commonly available on smartphones. Despite all these advances, this is only the beginning. The computer vision research community is actively working on much more challenging tasks, such as the fully automated understanding of images, which are bound to find their ways onto commercial progress in the not-so-distant future. Despite this progress, computer vision systems suffer from one major limitation: they have trouble adapting to the strongly-varying illumination conditions, especially outdoors. In particular, the sun, with its blinding intensity, creates effects such as highlights and cast shadows that are very difficult to deal with. While the human eye can easily adapt to those variations through millions of years of adaptation, the same cannot be said of digital cameras and of algorithms operating on their images. This research program will focus on the issue of allowing computer vision algorithms to adapt, and even leverage, the lighting conditions in an image. We will develop the new paradigm that jointly reasoning about illumination and scene components can help us better understand images. To do so, we will tackle the following four objectives. We will: 1) introduce new inference algorithms for jointly reasoning about face appearance and illumination conditions, to improve face detection in challenging lighting conditions; 2) develop new lighting models to capture the appearance of objects (people and cars) in a data-driven way to build better object detectors; 3) incorporate physical models of appearance to better segment images into semantic regions; and 4) provide a general framework for understanding how illumination is a useful cue in computer vision, even in the challenging world of consumer photographs. In addition to the applications mentioned above, the activities proposed in the context of this research program will also impact other fields, such as computer graphics, intelligent transportation, and robotics, all relevant to Canada. In computer graphics, additional information on the lighting conditions in images can contribute to improving applications such as pre-visualization, digital asset capture and management, and image editing. Safety features on intelligent transportation systems such as autonomous cars, which often rely on cameras for detecting obstacles on the road, strongly depend on their capacity to adapt to varying illumination conditions. Similarly, outdoor exploratory robots will also benefit from this research through the development of better models of outdoor illumination. The creation of this research program at Laval University will contribute to maintain and even expand Canada's relevance in these fields via its key technical contributions, and by training highly qualified personnel in its development.