A robust fitting algorithm for geometric computer vision problems under the L-norm optimisation framework is presented. It is essentially based on the maximum feasible subsystem (MaxFS) but it overcomes the computatio...
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A robust fitting algorithm for geometric computer vision problems under the L-norm optimisation framework is presented. It is essentially based on the maximum feasible subsystem (MaxFS) but it overcomes the computational limitation of the MaxFS for large data by finding only a quasi-maximum feasible subset. Experimental results demonstrate that the algorithm removes outliers more effectively than the other parameter estimation methods recently developed when the outlier-to-inlier ratio in a data set is high.
This paper proposes an active contour-based active appearance model (AAM) that is robust to a cluttered background and a large motion. The proposed AAM fittingalgorithm consists of two alternating procedures: active ...
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This paper proposes an active contour-based active appearance model (AAM) that is robust to a cluttered background and a large motion. The proposed AAM fittingalgorithm consists of two alternating procedures: active contour fitting to find the contour sample that best fits the face image and then the active appearance model fitting over the best selected contour. We also suggest an effective fitness function for fitting the contour samples to the face boundary in the active contour technique;this function defines the quality of fitness in terms of the strength and/or the length of edge features. Experimental results show that the proposed active contour-based AAM provides better accuracy and convergence characteristics in terms of RMS error and convergence rate than the existing robust AAM. The combination of the existing robust AAM and the proposed active contour-based AAM (AC-R-AAM) had the best accuracy and convergence performances. (c) 2006 Pattern Recognition Society. Published by Elsevier Ltd. All rights reserved.
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