Object recognition in digital photogrammetry

Object recognition and image understanding have increasingly become major subjects of interest for research activity in digital photogrammetry. This paper provides an overview of object recognition in photogrammetry, beginning with a problem statement and brief paradigm description. In order to exemplify the concept, automatic interior orientation is presented as an object recognition problem. Subsequent sections discuss the current status of object recognition by identifying relevant criteria, such as modelling, system strategies and inference components. Such criteria are useful for comparing object recognition systems or proposed approaches. Strengths and weaknesses of current systems are summarized, followed by a more detailed analysis of the modelling problem. Finally, two new approaches (scale-space and fusion of multisensor/multi-spectral data) are mentioned. These approaches serve as examples of promising new trends which have the potential of advancing object recognition to a new level.