Resection is one of important issues in machine vision.Although L2 norm based least square method is reasonably fast

the globally optimal solution cannot be obtained theoretically due to its non-convexity of the objective function.Optimization using the L norm has been becoming an effective way to solve parameter estimation problems in multiview geometry.But the computational cost increases rapidly with the size of measurement data.In the paper

we propose a novel approach under the framework of enhanced continuous taboo search (ECTS) for resection in multiview geometry.ECTS is an optimization method in the domain of artificial intelligence

which has an interesting ability of covering a wide solution space by promoting the search far away from current solution and consecutively decreasing the possibility of trapping in the local minima.We propose the corresponding ways in the key steps of ECTS

diversification and intensification.We also present theoretical proof to guarantee the global convergence of search with probability one.Experimental results validate that the ECTS can obtain the global optimum effectively and efficiently.Potentially

the novel ECTS framework can be employed in many applications of multi-view geometry.