This master thesis is about providing the implementation of synthesis, detection of spiral symbols and estimating the pan/tilt angle and position by using camera calibration. The focus is however on the latter, the estimation of parameters of localization. Spiral symbols are used to be able to give an object an identity as well as to locate it. Due to the spiral symbol´s characteristic shape, we can use the generalized structure tensor (GST) algorithm which is particularly efficient to detect different members of the spiral family. Once we detect spirals, we know the position and identity parameters of the spirals within an apriori known geometric configuration (on a sheet of paper). In turn, this information can be used to estimate the 3D-position and orientation of the object on which spirals are attached using a camera calibration method.   This thesis provides an insight into how automatic detection of spirals attached on a sheet of paper, and from this, automatic deduction of position and pose parameters of the sheet, can be achieved by using a network camera. GST algorithm has an advantage of running the processes of detection of spirals efficiently w.r.t detection performance and computational resources because it uses a spiral image model well adapted to spiral spatial frequency characteristic. We report results on how detection is affected by zoom parameters of the network camera, as well as by the GST parameters; such as filter size. After all spirals centers are located and identified w.r.t. their twist/bending parameter, a flexible technique for camera calibration, proposed by Zhengyou Zhang implemented in Matlab within the present study, is performed. The performance of the position and pose estimation in 3D is reported. The main conclusion is, we have reasonable surface angle estimations for images which were taken by a WLAN network camera in different conditions such as different illumination and different distances.