In this letter we propose a new fast matching algorithm that has no degradation of predicted images such as found in the conventional full search (FS) algorithm, so as to reduce the amount of computation of the FS algorithm for motion estimation in real-time video coding applications. That is, our proposing algorithm reduces only unnecessary computations in the process of motion estimation without decreasing the prediction quality compared to the conventional FS algorithm. The computational reduction comes from rapid elimination of impossible motion vectors. In comparison to the FS algorithm, we obtained faster elimination of inappropriate candidate motion vectors using efficient matching units based on image complexity. Experimentally, we demonstrated that the unnecessary computations were removed by about 30% as compared to the other fast FS algorithms.

While a standard Kalman filter (or α-β filter) is commonly used for target tracking, it is well known that the filter performance is often degraded when the target heavily maneuvers. The usual way to accommodate maneuver is to adaptively adjust the filter gain. Our aim is to reduce the tracking error during substantial maneuvering using a combination of non-traditional "intelligent" algorithms. In particular, we propose an effective gain control using fuzzy rule followed by position error compensation via neural network. A Monte-Carlo simulation is performed for various target paths of representative maneuvers employing the proposed algorithm. The results of the simulation indicate a significant improvement over conventional methods in terms of stability, accuracy, and computational load.

The watershed transform has been used as a powerful morphological segmentation tool in a variety of image processing applications. This is because it gives a good segmentation result if a topographical relief and markers are suitably chosen for different type of images. This paper proposes a parallel implementation of the watershed transform on the cellular neural network (CNN) universal machine, called cellular watersheds. Owing to its fine grain architecture, the watershed transform can be parallelized using local information. Our parallel implementation is based on a simulated immersion process. To evaluate our implementation, we have experimented on the CNN universal chip, ACE16k, for synthetic and real images.

In this paper, we demonstrate an immersive and interactive broadcasting production system with a new haptically enhanced multimedia broadcasting chain. The system adapts Augmented Reality (AR) techniques, which merges captured videos and virtual 3D media seamlessly through multimedia streaming technology, and haptic interaction technology in near real-time. In this system, viewers at the haptic multimedia client can interact with AR broadcasting production transmitted via communication network. We demonstrate two test applications, which show that the addition of AR- and haptic-interaction to the conventional audio-visual contents can improve immersiveness and interactivity of viewers with rich contents service.

Most of the traveller guidance services (TGS) are based on GPS technology and generally concerned with the position data mapping on the simplified 2D electronic map in order to provide macro level service facility such as drive direction notifications. Digital GIS based GPS entails in situ intuitive visualization. The visually enhanced TGS can improve the global and local awareness of unknown areas. In this paper, we propose a reliable new TGS system that provides 3D street as well as pin-pointed destination information in two stages of its interactive services; web-based and AR-based. The web server generates a guiding path on 2D digital map and displays 3D car-driving animation along the path. And, the AR-based service is embedded so that users can interactively obtain the detailed micro-level information of a specific section in the area with their fingertips. The implementation is based on autoformation of on-line GIS data structures from the available priori. For the verification, a 54 road network is selected as a test area. In the service demonstration, we show the effective awareness of street environments and the usefulness of this new TGS system.

We propose a new and fast full search (FS) motion estimation algorithm for video coding. The computational reduction comes from sequential rejection of impossible candidates with derived formula and subblock norms. Our algorithm reduces more the computations than the recent fast full search (FS) motion estimation algorithms.