Bringing Images to Light

长期以来，实际的计算机视觉应用一直局限于“机器视觉”领域：基于图像的自动检测和分析技术用于装配线。近年来，结合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”相机自动检测人脸并在人微笑时拍摄，搜索引擎成功地从文本查询中检索图像，在线数字地图使用街道级图像进行增强，并且增强现实应用程序通常可在智能手机上使用。尽管取得了这些进展，但这仅仅是个开始。计算机视觉研究界正在积极致力于更具挑战性的任务，例如完全自动化的图像理解，这必将在不久的将来实现商业化。 尽管取得了这些进展，但计算机视觉系统仍然受到一个主要限制：它们难以适应强烈变化的照明条件，特别是户外。特别是，太阳，其眩目的强度，创造的效果，如高光和投下的阴影，是非常难以处理。虽然人眼可以通过数百万年的适应轻松适应这些变化，但数码相机及其图像运算算法却不能这么说。 该研究计划将重点关注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，用于联合推理人脸外观和光照条件，以改善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，以捕获对象的外观（人和汽车）以数据驱动的方式构建更好的对象检测器; 3）结合外观的物理模型以更好地将图像分割成语义区域;和4）提供了一个一般框架，用于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。 除了上面提到的应用之外，在这个研究计划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，所有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可以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。智能交通系统（例如自动汽车）的安全功能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，户外探索机器人也将通过开发更好的户外照明模型从这项研究中受益。在拉瓦尔大学创建这一研究项目将有助于保持甚至扩大加拿大在这些领域的相关性，通过其关键的技术贡献，并通过培养高素质的人才在其发展。

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.