الفهرس 
CHAPTER (1): INTRODUCTIONOnly 14 pages are availabe for public view
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Abstract
Nowadays, photogrammetry is the most important science for data collection and large data storage. Photogrammetric science became important and reliable tool for many applications such as mapping, architecture, medicine, geology, environment and In order to achieve the reliability of photogrammetry, the accuracy of Photogrammetric systems were widely studied and there were lots of trials to optimize the accuracy by developing new tools and techniques. This research contains four objectives. The first objective is to use the geometry of multi baseline stations concept and the direction cosine to determine the object space coordinates for the test field points. The second objective is to investigate the possibility of using photomodeler software to obtain good three-dimensional object point coordinates, camera parameters must be obtained. The third objective is to study the different techniques that can be used to solve the problem of where to place the cameras in order to obtain minimal error in the 3D measurements, also called camera network in photogrammetry. The first technique was used is a multi-cellular genetic algorithm. Practical and simulated tests have been carried out to study the feasibility of the genetic algorithm for determining the optimum camera position. The final objective of this research work is to solve the problem of where to place the cameras using the particle swarm algorithm, in order to obtain -IIminimal error in the 3D measurements. The solution steps followed for solving the optimization theory are given. Based on the obtained results, it can be recommended that, the location of the camera networks must be designed according to the optimization theory. The genetic algorithm, as a non conventional optimization technique can optimize the placement of the cameras in a station so that these cameras can be used to take measurements within the required accuracy. The particle swarm algorithm can be used to obtain the most favorable position of the camera when compared to the genetic algorithm.