Learning detailed models from images and videos using machine learning techniques and applications to graphics

使用机器学习技术和图形应用从图像和视频中学习详细模型

• 批准号: 341585-2007
• 负责人: Bouguila, Nizar
• 金额: $ 1.38万
• 依托单位: Concordia University
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2008
• 资助国家: 加拿大
• 起止时间: 2008-01-01 至 2009-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=399483
• 关键词: Learning; detailed; models; images; videos

Synthetic images and animations of real scenes are now common place in different domains such as medicine, computer games, feature films, TV advertising, homeland security, and fine arts. Creating these images and animation is the first goal of computer graphics. Traditional computer graphics approaches start with geometric models and then generate and display virtual representations. Many computer graphics approaches have been successful. However, it is clear that synthetic images and animation still look artificial and that the cost and time have to be lowered. In the past, the field of computer graphics has been considered as the inverse of computer vision. Indeed, computer vision starts with input images and videos and process them to understand the geometric and physical properties of objects and scenes. The objectives of my research are the integration of computer vision and computer graphics techniques, and the creation of a framework in which these two domains collaborate through machine learning techniques to model the world around us (e.g. human body and motion, natural scenes, rigid and non-rigid objects) directly from measurements. These measurements will be learned from real images and scenes. Indeed, recent years have seen a significant technological development in the areas of high-quality sensors which have simplified the acquisition of the world content.

真实的场景的合成图像和动画现在常见于不同的领域，例如医学、计算机游戏、故事片、电视广告、国土安全和美术。创建这些图像和动画是计算机图形学的首要目标。 传统的计算机图形学方法从几何模型开始，然后生成和显示虚拟表示。许多计算机图形学方法已经取得了成功。然而，很明显，合成图像和动画看起来仍然是人工的，并且必须降低成本和时间。 在过去，计算机图形学领域一直被认为是计算机视觉的逆。事实上，计算机视觉从输入图像和视频开始，并对其进行处理，以了解物体和场景的几何和物理特性。我的研究目标是整合计算机视觉和计算机图形技术，并创建一个框架，在这个框架中，这两个领域通过机器学习技术合作，直接从测量中建模我们周围的世界（例如人体和运动，自然场景，刚性和非刚性物体）。这些测量将从真实的图像和场景中学习。 事实上，近年来在高质量传感器领域取得了重大的技术发展，这些传感器简化了世界内容的获取。