A novel system for real-time tumor tracking and motion compensation with a robotic HexaPOD treatment couch is described. The approach is based on continuous tracking of the tumor motion in portal images without implanted fiducial markers, using the therapeutic megavoltage beam, and tracking of abdominal breathing motion with optical markers. Based on the two independently acquired data sets the table movements for motion compensation are calculated. The principle of operation of the entire prototype system is detailed first. In the second part the performance of the HexaPOD couch was investigated with a robotic four-dimensional-phantom capable of simulating real patient tumor trajectories in three-dimensional space. The performance and limitations of the HexaPOD table and the control system were characterized in terms of its dynamic behavior. The maximum speed and acceleration of the HexaPOD were 8 mm/s and 34.5 mm/s2 in the lateral direction, and 9.5 mm/s and 29.5 mm/s2 in longitudinal and anterior-posterior direction, respectively. Base line drifts of the mean tumor position of realistic lung tumor trajectories could be fully compensated. For continuous tumor tracking and motion compensation a reduction of tumor motion up to 68% of the original amplitude was achieved. In conclusion, this study demonstrated that it is technically feasible to compensate breathing induced tumor motion in the lung with the adaptive tumor tracking system.

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Recently, several backpack-mounted systems, also known as personal laser scanning systems, have been developed. They consist of laser scanners or cameras that are carried by a human operator to acquire measurements of the environment while walking. These systems were first designed to overcome the challenges of mapping indoor environments with doors and stairs. While the human operator inherently has the ability to open doors and to climb stairs, the flexible movements introduce irregularities of the trajectory to the system. To compete with other mapping systems, the accuracy of these systems has to be evaluated. In this paper, we present an extensive evaluation of our backpack mobile mapping system in indoor environments. It is shown that the system can deal with the normal human walking motion, but has problems with irregular jittering. Moreover, we demonstrate the applicability of the backpack in a suitable urban scenario.

Recent years have seen an explosion in the use of invariant keypoint methods across nearly every area of computer vision research. Since its introduction, the scale-invariant feature transform (SIFT) has been one of the most effective and widely-used of these methods and has served as a major catalyst in their popularization. In this paper, I present an open-source SIFT library, implemented in C and freely available at http://eecs.oregonstate.edu/~hess/sift.html, and I briefly compare its performance with that of the original SIFT executable released by David Lowe.

The task of registering video frames with a static model is a common problem in many computer vision domains. The standard approach to registration involves finding point correspondences between the video and the model and using those correspondences to numerically determine registration transforms. Current methods locate video-to-model point correspondences by assembling a set of reference images to represent the model and then detecting and matching invariant local image features between the video frames and the set of reference images. These methods work well when all video frames can be guaranteed to contain a sufficient number of distinctive visual features. However, as we demonstrate, these methods are prone to severe misregistration errors in domains where many video frames lack distinctive image features. To overcome these errors, we introduce a concept of local distinctiveness which allows us to find model matches for nearly all video features, regardless of their distinctiveness on a global scale. We present results from the American football domain-where many video frames lack distinctive image features-which show a drastic improvement in registration accuracy over current methods. In addition, we introduce a simple, empirical stability test that allows our method to be fully automated. Finally, we present a registration dataset from the American football domain we hope can be used as a benchmarking tool for registration methods.