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。尽管这一难题的完全通用解决方案仍需数年时间，但在对该技术的实际应用产生直接影响的不同方面取得了定期进展。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，即在大范围的距离和视角上检测和识别物体的可能性，改进算法以适当覆盖一般类型的物体，以及减少运行程序所需的时间，来进一步做出贡献。在一些重要问题上取得了进展，这些问题导致了强大，高效和有效的自动认知计算机视觉方法。当应用程序需要在只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，该研究计划旨在帮助实现安全，运输，媒体，医疗保健和教育领域的大量不同应用，这些应用都具有很高的社会经济影响。图像理解方法通常试图再现人类的表现，而不建模和估计底层大脑过程的参数。因此，在开发和测试认知计算机视觉方法时，需要并必须遵循适当且独特的方法。最近，我们定义了这样一种方法，作为我们努力改进认知计算机视觉方法开发、记录和评估方式的一部分。七名研究生将从事这项研究计划。过去的学生在大学，研究实验室和高科技行业任职。

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.