CAREER: Image Variability Decomposition for Recognition, Reconstruction, and Tracking

本研究探讨如何在照明，姿态和形状的对象的变化产生的变化，在观察到的图像。 其中心思想是，物体图像的可变性可以分解为组成部分-照明，姿态和形状-其中每一个，当单独分析时，表现良好。 这 范例， 被称为“图像可变性分解”，与基于外观的范例的实质不同之处在于，通过分解可变性，人们能够揭示可变性的每个源上的图像集合的生成结构。 因此，与基于外观的方法不同，它不需要在照明条件、姿势和形状的所有看似无限可能的排列下看到对象。 相反，图像可变性的每个分量被明确地建模。 这项研究被应用到问题中，这种变化起着重要的作用：人脸识别，变化检测，结构从运动，视觉跟踪。 该奖项的教育部分侧重于实验室和项目 密集 教学 学生，两者 研究生 和本科生，不仅教已知的未解决的问题，但鼓励考虑提出新的问题，在计算机视觉，机器人和模式识别。 学生们也会因为在新领域找到新的应用而受到奖励。 教育活动将围绕耶鲁大学新成立的计算视觉与控制中心内的计算机视觉实验室的建设，计算机视觉和模式与物体识别实验室强化课程的设计，以及计算机视觉教科书的完成。

This research investigates how changes in illumination, pose, and shape of an object produce changes in the observed images. The central idea is that the variability in the images of an object can be decomposed into component parts - illumination, pose, and shape - each of which, when analyzed separately, is well behaved. This paradigm, termed ``Image Variability Decomposition,'' differs substantially from the appearance-based paradigm in that by decomposing the variability, one is able to uncover generative structures to the set of images over each source of the variability. Thus, unlike appearance-based methods, it is not necessary to have seen the object under all of the seemingly infinite possible permutations of lighting conditions, pose, and shape. Instead, each component of the image variability is explicitly modeled. This research is applied to problems in which this variability plays an important role: face recognition, change detection, structure from motion, and visual tracking. The education component of this award focuses on laboratory and project intensive teaching. Students, both graduate and undergraduate, are not only taught about known unsolved problems, but are encouraged to consider posing new problems in computer vision, robotics, and pattern recognition. Students are also rewarded for finding new applications in new domains. The education activities will center around the construction of a computer vision laboratory within Yale University's newly formed Center for Computational Vision and Control, the design of laboratory intensive courses on both computer vision and pattern and object recognition, and the completion of a textbook on computer vision.