Today's computer vision applications often have to deal with multiple, uncertain, and incomplete visual information. In this paper, we apply a new method, termed 'active fusion', to the problem of generic object recognition. Active fusion provides a common framework for active selection and combination of information from multiple sources in order to arrive at a reliable result at reasonable costs. In our experimental setup we use a camera mounted on a 2m by 1.5m x/z-table observing objects placed on a rotating table. Zoom, pan, tilt, and aperture setting of the camera can be controlled by the system. We follow a part-based approach, trying to decompose objects into parts, which are modeled as geons. The active fusion system starts from an initial view of the objects placed on the table and is continuously trying to refine its current object hypotheses by requesting additional views. The implementation of active fusion on the basis of probability theory, Dempster-Shafer's theory of evidence and fuzzy set theory is discussed. First results demonstrating segmentation improvements by active fusion are presented.
This paper reports results from interdisciplinary research: a framework for the integration of multiple information in image analysis called 'information fusion in image understanding' is applied to provide a new visualization scheme for diagnosis and treatment of the human retina. This framework deals with representations and processes at all levels of abstraction. It is used to represent anatomical and pathological knowledge, to extract significant features from the input channels, and to obtain a complex diagnosis by means of fusion. Each patient is examined in six steps using a scanning laser ophthalmoscope (SLO) providing several spectral channels and aperture settings, as well as static scotometry to measure scotoma (areas with a loss of visual function). Feature extraction processes yield dark (fovea) and bright (leakage) blobs at several scales,clusters of scotoma measures, tubes (blood vessels), and circular areas (optic disc) in six different image description. By affine matching, the fusion system establishes spatial relationships between the features of each image description. Spatial reasoning using these relationships leads to a fusion of symbolic information without the necessity or prior manual registration of the input channels. Finally, the extracted features are arranged in different overlays to visualize a 'map of anatomical features and pathological changes.' SLO examinations using the above scheme were carried out with more than 50 patients suffering from age-related macular degeneration at the Vienna Eye Clinic. The application of information fusion has now lead to a new concept about the nature of the disease, to new diagnostic capabilities, and to a clear distinction between three different classes of therapy.