Fundamental issues in cognitive computer vision

该研究项目属于认知计算机视觉领域。认知计算机视觉的研究人员开发了数学模型、算法和计算机程序，以自动确定数字图像和视频中出现的对象和场景的类型。尽管对这一难题的全面解决还需要数年时间，但在对该技术在现实世界中的应用产生直接影响的不同方面，仍在不断取得进展。这项研究提案旨在通过解决与研究人员面临的关键问题相关的基本问题来进一步做出贡献，即检测和识别大范围距离和视点上的对象的可能性，改进算法以适当地覆盖一般类型的对象，以及减少运行程序所需的时间。在一些重要问题上将取得进展，从而导致稳健、高效和有效的自动认知计算机视觉方法。每当应用程序需要在仅允许有限的特定期望的上下文中理解图像内容时，就需要这些。因此，这项研究计划旨在帮助实现安全、交通、媒体、医疗保健和教育领域的大量不同应用，所有这些都具有很高的社会经济影响。图像理解方法通常试图在不对潜在的大脑过程进行建模和估计参数的情况下重现人类的行为。因此，在开发和测试认知计算机视觉方法时，需要一种适当和独特的方法，并且必须遵循这种方法。最近，我们将这种方法定义为我们改进认知计算机视觉方法开发、记录和评估方式的努力的一部分。七名研究生将参与这项研究计划。过去的学生在大学、研究实验室和高科技行业担任职位。

This research program is in the field of cognitive computer vision. Researchers in cognitive computer vision develop mathematical models, algorithms, and computer programs to automatically determine what types of objects and scenes appear in digital images and videos. Even though a completely general solution to this difficult problem is still years away, regular progress is made on different aspects that have an immediate impact on real-world applications of the technology. This research proposal intends to contribute further by addressing fundamental issues related to key questions facing the researchers, namely the possibility to detect and recognize objects over a large range of distances and viewpoints, the improvement of the algorithms to cover appropriately general types of objects, and the reduction of the time it takes to run the programs. Progress is to be made on a number of important issues leading to robust, efficient, and effective automatic cognitive computer vision methods. These are required whenever an application necessitates an understanding of the image contents within a context that allows only limited specific expectations. As such, this research program is to help actualize a large number of different applications in security, transport, media, health care, and education fields, all with high socioeconomic impact. Image understanding methods usually attempt to reproduce human performance without modeling and estimating parameters of the underlying brain processes. For this reason, a proper and distinct methodology is required and must be followed in developing and testing cognitive computer vision methods. Recently, we defined such a methodology as part of our effort to improve the way cognitive computer vision methods are developed, documented, and evaluated. Seven graduate students are to work on the research program. Past students are holding positions in universities, research laboratories, and high-tech industries.