Moving cast shadows are a major concern for foreground detection algorithms. The processing of foreground images in surveillance applications typically requires that such shadows be identified and removed from the detected foreground. This paper presents a novel pixel-based statistical approach to model moving cast shadows of nonuniform and varying intensity. This approach uses the Gaussian mixture model (GMM) learning ability to build statistical models describing moving cast shadows on surfaces. This statistical modeling can deal with scenes with complex and time-varying illumination, including light saturated areas, and prevent false detection in regions where shadows cannot be detected. The proposed approach can be used with pixel-based descriptions of shadowed surfaces found in the literature. It significantly reduces their false detection rate without increasing the missed detection rate. Results obtained with different scene types and shadow models show the robustness of the approach.
We present an algorithm, based on simulated annealing, for automatic seed matching and three-dimensional spatial coordinate reconstruction using either three radiographic films or three fluoroscopic images taken from different perspectives. The matching problem is defined in the framework of combinatorial optimization, which allows robust reconstruction in presence of calibration imprecision, patient movements, and isometric distortions. Furthermore, by using a global criterion to select the correct matching, we evade common problems of the three-film method and its variants in presence of noise. The algorithm has been tested on 112 clinical cases and 100 simulated implants and used clinically on more than 100 cases. Simulated implants were reconstructed with an average error of 0.21 mm. For clinical cases, comparison of the precision is performed between results obtained with this new method and results obtained using the three-film technique. Compared to the latter technique, the reconstruction precision was improved in 62% of the clinical cases.
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