Static and Dynamic Image Analysis and Synthesis

静态和动态图像分析与合成

• 批准号: RGPIN-2014-06210
• 负责人: Yang, Herbert
• 金额: $ 2.84万
• 依托单位: University of Alberta
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2015
• 资助国家: 加拿大
• 起止时间: 2015-01-01 至 2016-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=588871
• 关键词: Static; Dynamic; Image; Analysis; Synthesis

Computer vision (CV) focuses on using mathematical methods to extract the shape, structure, semantic information or other intrinsic properties of a scene. The goal of computer graphics (CG) is the reverse process of generating desired images, photographic or artistic, using computer algorithms. CV and CG are intimately related. Hence, research results in one area can often be applied or extended to the other area. My long term research goal is to develop a process or processes that can facilitate the interchange of results between CV and CG. During the last 5 years, when my students and I were developing an underwater 8-camera array system for Neptune Canada, we discovered that many previous works in underwater imaging have simplified incorrectly the refraction effect. Since I am developing the next generation of underwater 3D vision system with Venus Canada and since there is very little work in physically correct underwater computer vision algorithms, I plan to focus my efforts on issues related to underwater imaging by developing physically correct methods. In the short term, I plan to focus on the following objectives within the next five years: 1) to develop a physically correct framework for developing underwater computer vision algorithms, 2) to use the framework to develop physically correct underwater computer vision algorithms, in particular, camera calibration and 3D reconstruction, 3) to use the framework to develop new algorithms in synthetic aperture imaging and in structured light techniques for the underwater environment, and 4) to extract attributes or features that can help to animate fluid more realistically. To achieve objective 1, I plan to investigate different mathematical frameworks that can address the issues of refraction. The goal is to develop a set of mathematical tools similar to what has been done for land-based systems. For objective 2, I plan to use the framework developed in objective 1 to derive select computer vision algorithms, in particular, camera calibration and 3D reconstruction. As well, I will demonstrate the validity of these algorithms experimentally. The next problem that I will investigate is in 3D reconstruction. The success of land-based computer vision algorithms is due to the discovery and the use of constraints, e.g. epipolar, optical flow, left-right consistency, to name a few. In the underwater environment, I will extend constraints used for land-based system to the underwater environment and to develop new ones. The plan for objective 3 is to investigate two major computer vision topics, namely, synthetic aperture imaging (SAI) and structured light. For SAI, I will extend my recent work to develop one for the underwater environment. One advantage of SAI is its ability to see through occlusion, which is very useful when some portion of a camera is occluded. An underwater structured light system will provide much improvement to the accuracy of the correspondence process. Realistic rendering of fluid is closely related to the above mentioned work. On land, using computer vision algorithms, extracting the intrinsic properties of a scene can enhance the quality of rendered images. By the same token, for the underwater environment, the extracted attributes of the underwater environment will enhance the quality of rendered images. Since there is very little work done in underwater computer vision and computer graphics, my work will certainly have significant short as well as long term impact to the research community.

计算机视觉（CV）侧重于使用数学方法来提取场景的形状，结构，语义信息或其他内在属性。计算机图形学（CG）的目标是使用计算机算法生成所需图像（摄影或艺术）的逆过程。CV和CG是密切相关的。因此，一个领域的研究成果往往可以应用或扩展到另一个领域。 我的长期研究目标是开发一个或多个过程，可以促进CV和CG之间的结果交换。在过去的5年里，当我和我的学生为Neptune Canada开发水下8相机阵列系统时，我们发现许多以前的水下成像工作都错误地简化了折射效应。 由于我正在与Venus Canada一起开发下一代水下3D视觉系统，并且由于物理正确的水下计算机视觉算法的工作很少，因此我计划通过开发物理正确的方法将精力集中在与水下成像相关的问题上。在短期内，我计划在未来五年内重点实现以下目标：1）开发用于开发水下计算机视觉算法的物理上正确的框架，2）使用该框架来开发物理上正确的水下计算机视觉算法，特别是相机校准和3D重建，3）使用该框架开发用于水下环境的合成孔径成像和结构光技术中的新算法，以及4）提取可以帮助更逼真地动画流体的属性或特征。 为了实现目标1，我计划研究不同的数学框架，可以解决折射的问题。目标是开发一套类似于陆基系统的数学工具。 对于目标2，我计划使用目标1中开发的框架来导出选择计算机视觉算法，特别是相机校准和3D重建。同时，我将通过实验证明这些算法的有效性。 我将研究的下一个问题是3D重建。基于陆地的计算机视觉算法的成功是由于发现和使用约束，例如极线，光流，左右一致性，仅举几例。在水下环境中，我将用于陆基系统的约束扩展到水下环境，并开发新的约束。目标3的计划是研究两个主要的计算机视觉主题，即合成孔径成像（SAI）和结构光。对于SAI，我将扩展我最近的工作，为水下环境开发一个。SAI的一个优点是它能够穿透遮挡，这在相机的某些部分被遮挡时非常有用。水下结构光系统将大大提高对应过程的精度。 流体的真实感绘制与上述工作密切相关。在陆地上，使用计算机视觉算法，提取场景的内在属性可以提高渲染图像的质量。同样的道理，对于水下环境，提取的水下环境的属性将提高渲染图像的质量。由于水下计算机视觉和计算机图形学方面的工作很少，我的工作肯定会对研究界产生重大的短期和长期影响。