@inproceedings{SchmidtVogtNiemann2005, author = {Schmidt, Jochen and Vogt, F. and Niemann, H.}, title = {Vector Quantization Based Data Selection for Hand-Eye Calibration}, series = {Vision, Modeling, and Visualization 2004, pages 21-28, Stanford, USA}, booktitle = {Vision, Modeling, and Visualization 2004, pages 21-28, Stanford, USA}, year = {2005}, abstract = {The paper presents a new vectorquantization based Approach for selecting well-suited data for hand-eye calibration from a given sequence of hand and eye movements. Data selection is essential if control of the movements used for calibration is not possible, especially when using continuously recorded data. The new algorithm is compared to another method for data selection as well as to the processing of subsequent movements. Experimental results on real and synthetic data sets show the superior performance of the new approach with respect to calibration errors and computation time. Real data has been obtained from an optical tracking system and a camera mounted on an endoscope, the goal being the reconstruction of medical lightfields.}, language = {en} } @inproceedings{ZinsslerSchmidtNiemann2005, author = {Zinßler, T. and Schmidt, Jochen and Niemann, H.}, title = {Point Set Registration with Integrated Scale Estimation}, series = {Proceedings of the Eighth International Conference on Pattern Recognition and Image Processing (PRIP 2005), pages 116-119, Minsk, Republic of Belarus}, booktitle = {Proceedings of the Eighth International Conference on Pattern Recognition and Image Processing (PRIP 2005), pages 116-119, Minsk, Republic of Belarus}, year = {2005}, abstract = {Wepresentaniterativeregistrationalgorithmfor aligning two differently scaled 3-D point sets. It extends the popular Iterative Closest Point (ICP) algorithm by estimating a scale factor between the two point sets in every iteration. The presented algorithm is especially useful for the registration of point sets generated by structure-frommotion algorithms, which only reconstruct the 3-D structure of a scene upto scale. LiketheoriginalICPalgorithm,thepresentedalgorithm requires a rough pre-alignment of the point sets. In order to determine the necessary accuracy of the pre-alignment, wehaveexperimentallyevaluatedthebasinofconvergence of the algorithm with respect to the initial rotation, translation, andscale factor between the two point sets.}, language = {en} } @inproceedings{SchmidtVogtNiemann2005, author = {Schmidt, Jochen and Vogt, F. and Niemann, H.}, title = {Calibration-Free Hand-Eye Calibration: A Structure-from-Motion Approach}, series = {Joint Pattern Recognition Symposium}, booktitle = {Joint Pattern Recognition Symposium}, publisher = {Springer}, address = {Berlin}, year = {2005}, abstract = {The paper presents an extended hand-eye calibration approach that, in contrast to the standard method, does not require a calibration pattern for determining camera position and orientation. Instead, a structure-from-motion algorithm is applied for obtaining the eye-data that is necessary for computing the unknown hand-eye transformation. Different ways of extending the standard algorithm are presented, which mainly involves the estimation of a scale factor in addition to rotation and translation. The proposed methods are experimentally compared using data obtained from an optical tracking system that determines the pose of an endoscopic camera. The approach is of special interest in our clinical setup, as the usage of an unsterile calibration pattern is difficult in a sterile environment.}, language = {en} }