Bringing Images to Light

长期以来，实际的计算机视觉应用发现自己局限于“机器视觉”领域：用于装配线上自动检查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”相机可以自动检测人脸并在人们微笑时进行拍摄，搜索引擎可以成功地从文本查询中检索图像，在线数字地图可以通过街道图像进行增强，增强现实应用程序在智能手机上普遍可用。尽管有这些进步，但这仅仅是个开始。计算机视觉研究界正在积极研究更具挑战性的任务，比如完全自动化的图像理解，这些任务必然会在不久的将来取得商业进展。尽管取得了这些进展，计算机视觉系统仍有一个主要的限制：它们难以适应强烈变化的照明条件，尤其是户外。特别是，太阳，其令人眩目的强度，创造的效果，如高光和投射的阴影，非常难以处理。经过数百万年的适应，人眼可以很容易地适应这些变化，但数码相机和处理图像的算法却不是这样。这个研究项目将专注于让计算机视觉算法适应甚至利用图像中的照明条件。我们将开发新的范例，联合推理照明和场景组件可以帮助我们更好地理解图像。为此，我们将努力实现以下四个目标：我们将：1)引入新的推理算法，对人脸外观和光照条件进行联合推理，以提高在恶劣光照条件下的人脸检测；2)开发新的照明模型，以数据驱动的方式捕捉物体（人和汽车）的外观，以构建更好的物体探测器；3)结合外观的物理模型，更好地将图像分割到语义区域；4)提供一个总体框架来理解照明如何在计算机视觉中是一个有用的线索，即使在具有挑战性的消费者照片世界中也是如此。除了上面提到的应用之外，在这个研究项目的背景下提出的活动也将影响其他领域，如计算机图形学，智能交通和机器人技术，这些都与加拿大有关。在计算机图形学中，关于图像中照明条件的附加信息有助于改进诸如预可视化、数字资产捕获和管理以及图像编辑等应用程序。自动驾驶汽车等智能交通系统的安全功能通常依赖于摄像头来探测道路上的障碍物，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受益于这项研究。在拉瓦尔大学设立这一研究项目将有助于通过其关键技术贡献和培养高素质人才，维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.