This paper introduces a new fitting approach to allow an efficient part-by-part reconstruction or update of editable CAD models fitting the point cloud of a digitized mechanical parts ′ assembly. The idea is to make use of parameterized CAD models whose dimensional parameters are to be optimized to match the acquired point cloud. Parameters may also be related to assembly constraints, e.g. the distance between two parts. The optimization kernel relies on a simulated annealing algorithm to find out the best values of the parameters so as to minimize the deviations between the point cloud and the CAD models to be fitted. Both global and local fitting are possible. During the optimization process, the orientation and positioning of the CAD parts are driven by an ICP algorithm. The modifications are ensured by the batch calls to a CAD modeler which updates the models as the fitting process goes on. The modeler also handles the assembly constraints. Both single and multiple parts can be fitted, either sequentially or simultaneously. The evaluation of the proposed approach is performed using both real scanned point clouds and as-scanned virtually generated point clouds which incorporate several artifacts that could appear with a real scanner. Results cover several Industry 4.0 related application scenarios, ranging from the global fitting of a single part to the update of a complete Digital Mock-Up embedding assembly constraints. The proposed approach demonstrates good capacities to help maintaining the coherence between a product/system and its digital twin.
Ocular biometrics refers to the use of features of the eye for person recognition. For instance, the unique and stable texture of the iris has been recognised as a powerful ocular biometric characteristic. In this study, the authors propose to improve biometric authentication with a multimodal ocular biometric system based on the iris pattern and the three-dimensional shape of the cornea. They show how the cornea can be used as a biometric trait for person recognition and then, they propose an intra-ocular fusion with iris features to improve the overall performance of the system. Feature extraction was done by modelling the shape of the cornea with a Zernike polynomial expansion. Then the best linear combinations of Zernike coefficients were found with linear discriminant analysis and used as biometric identifier. The iris texture was analysed with a typical methodology using Gabor filtering and phase encoding. The fusion was performed at the matching score level using min, max, sum and weighted-sum rule. The experimental results on a new database constructed for this bi-modal study showed impressive performance of the proposed ocular biometric system with equal error rate decreasing to 0% with the weighted-sum rule.
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