Fundamental issues in cognitive computer vision

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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.