This paper presents a new method to recover the relative pose between two images, using three points and the vertical direction information. The 1 arXiv:0905.3964v1 [cs.CV] 25 May 2009 vertical direction can be determined in two ways: 1-using direct physical measurement like IMU (inertial measurement unit), 2-using vertical vanishing point. This knowledge of the vertical direction solves 2 unknowns among the 3 parameters of the relative rotation, so that only 3 homologous points are requested to position a couple of images. Rewriting the coplanarity equations leads to a simpler solution. The remaining unknowns resolution is performed by an algebraic method using Gröbner bases. The elements necessary to build a specific algebraic solver are given in this paper, allowing for a real-time implementation. The results on real and synthetic data show the efficiency of this method.
The accuracy of artificial neural networks (ANNs), adaptive neuro-fuzzy inference system (ANFIS) and gene expression programming (GEP) in modeling dissolved oxygen (DO) concentration was investigated in this study. Water temperature, specific conductance, pH, discharge and DO concentration data from South Platte River at Englewood, Colorado were used. Various input combinations of these data were tried as inputs to the ANN and ANFIS methods. The ANN and ANFIS models with the water temperature, specific conductance, pH and discharge input parameters performed the best. The optimal GEP model was obtained for the best input combination and compared with the ANN and ANFIS models with respect to correlation coefficient, root mean square error, mean absolute error and mean absolute relative error criteria. Results revealed that the GEP model performed better than the ANN and ANFIS models in modeling DO concentration.
The main purpose of this paper is to improve the bound of complexity of the well-known algorithms on polynomial ideals having complexities polynomial in dn, where d is the maximal degree of input polynomials and n is the number of variables. Instead of this bound, we present the more accurate bound max {S, Dn} where S is the size of the input polynomials in dense representation, and D is the arithmetic mean value of the degrees of input polynomials.
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