This article introduces a new curve reconstruction method based on recovering the control points of parametric cubic curves. The method developed here has two stages: finding the 3D control points of parametric curves...
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This article introduces a new curve reconstruction method based on recovering the control points of parametric cubic curves. The method developed here has two stages: finding the 3D control points of parametric curves and reconstruction of free curves. The 3D control points of curves are computed from 2D image sequences by using projective reconstruction of the 3D control points and the bundle adjustment algorithm. The relationships among parametric curves, such as Hermite curves, Bézier curves and B-spline curves, are established so that a curve of any model can be achieved for best fitting. Some experiments are performed to show the performance and effectiveness of the algorithm. The method is based on the slope following and learning algorithm, which provides an efficient way of finding the 3D control points of any type of cubic Bézier curves. This method, which is an extension of our previous work on recovering control points of 2D Bézier curves, can automatically fit a set of data points with piecewise geometrically continuous cubic parametric curves. The experimental results demonstrate that our method is a fast and efficient way of recovering 3D control points of parametric curves, matching free curves and shape reforming.
Recognizing bio-signals, such as EMG, EEG, EOG and ECG, is a promising theme of study since it provides with a convenient means for human-machine interaction. Various approaches of determining features of bio-signals ...
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For successful assembly of flexible parts, informations about their deformation as well as possible misalignments between the holes and their mating parts are essential, since the corrective assembly motion to compens...
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For successful assembly of flexible parts, informations about their deformation as well as possible misalignments between the holes and their mating parts are essential, since the corrective assembly motion to compensate for such misalignments has to be determined from the measured informations. However, the relationship between them is very complex, and thus cannot be simply derived from analytical methods. This paper presents a neural net-based inference system that can infer the complex relationship between the corrective motion and the measured information of parts deformation and misalignments. The lateral misalignment and the inclination angle of a part are given as the inputs, and the lateral corrective motion is given as the output for the neural network. By using the proposed method, a series of experiments to compensate for the lateral misalignment are performed. Experimental results show that the proposed neural net-based assembly algorithm is effective in compensating for the lateral misalignment from the point of view of a fast search toward the hole center, and that it can be extended to the assembly tasks under more general conditions.
A hierarchial classification and identification model for fingerprint matching is presented. The proposed system categorizes the fingerprint into initial classes. The model uses enhanced fingerprints obtained by frequ...
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