No abstract

Abstract. We present an algorithm capable of making in real time image mosaics with enlarged field-of-view from the endoscopic video data stream. The algorithm is based on the method of Kourogi et al. (1999) which we extend to the case of endoscopic masks. The algorithm automatically finds the optimal affine transform between video frames and builds the enlarged field-of-view as an intervention-free side task. We apply our algorithm to endoscopic video sequences and compare it to the well-known image-mosaicing algorithm of Szeliski (1994). Our method turns out to be more robust, more than 3 times faster, having at the same time a 4 times smaller average motion estimation error: 0.19 pixel instead of 0.72 pixel between successive frames.

Cyclone separators are filtration devices frequently used in industry, e.g., to filter particles from flue gas. Optimizing the cyclone geometry is a demanding task. Accurate simulations of cyclone separators are based on time consuming computational fluid dynamics simulations. Thus, the need for exploiting cheap information from analytical, approximative models is evident. Here, we employ two multi-objective optimization algorithms on such cheap, approximative models to analyze their optimization performance on this problem. Under various limitations, we tune both algorithms with Sequential Parameter Optimization (SPO) to achieve best possible results in shortest time. The resulting optimal settings are validated with different seeds, as well as with a different approximative model for collection efficiency. Their optimal performance is compared against a model based approach, where multi-objective SPO is directly employed to optimize the Cyclone model, rather than tuning the optimization algorithms. It is shown that SPO finds improved parameter settings of the concerned algorithms and performs excellently when directly used as an optimizer.

No abstract