Camera mohon estmation is useful for a range of apphcations. Usually, feature tracking is performed through the sequence of images to determine correspondences. Furthermore, robust statist~cal techniques are normally used to handle large number of outliers in correspondences. This paper proposes a new method that avoids both. Motion is calculated between two consecutive stereo images without any pre-knowledge or prediction about feature location or the possibly large camera movement. This permits a lower kame rate and almost arbitrary movements. Euclidean constraints are used to incrementally select inliers from a set of mitial correspondences, instead of using robust statistics that has to handle all inliers and outhers together. These consi"are so strong that the set of initial correspondences can contain several times more outliers than inliers.Experiments on a worst-case stereo sequence show that the method is robust accurate and can be used in real-time.

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This paper describes a fuzzy approach to computer-aided medical diagnosis in a clinical context. It introduces a formal view of diagnosis in clinical settings and shows the relevance and possible uses of fuzzy cognitive maps. A constraint satisfaction method is introduced that uses the temporal uncertainty in symptom durations that may occur with particular diseases. The method results in an estimate of the stage of the disease if the temporal constraints of the disease in relation to the occurrence of the symptoms are satisfied. A lightweight fuzzy process is described and evaluated in the context of diagnosis of two confusable diseases. The process is based on the idea of an incremental simple additive model for fuzzy sets supporting and negating particular diseases. These are combined to produce an index of support for a particular disease. The process is developed to allow fuzzy symptom information on the intensity and duration of symptoms. Results are presented showing the effectiveness of the method for supporting differential diagnosis.

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