Operations, such as text entry and zooming, are simple and frequently used on mobile touch devices. However, these operations are far from being perfectly supported. In this paper, we present our prototype, BackAssist, which takes advantage of back-of-device input to augment front-of-device touch interaction. Furthermore, we present the results of a user study to evaluate whether users can master the back-of-device control of BackAssist or not. The results show that the back-of-device control can be easily grasped and used by ordinary smart phone users. Finally, we present two BackAssist supported applications - a virtual keyboard application and a map application. Users who tried out the two applications give positive feedback to the BackAssist supported augmentation.

In this paper, we propose a filtering approach based on global motion estimation (GME) and global motion compensation (GMC) for pre- and postprocessing of video codecs. For preprocessing a video codec, group of pictures (GOP), which is a basic unit for GMC, and reference frames are first defined for an input video sequence. Next, GME and GMC are sequentially performed for every frame in each GOP. Finally, a block-based adaptive temporal filter is applied between the GMC frames before video encoding. For postprocessing a video codec at the decoder end, every decoded frame is inversely motion-compensated using the transmitted global motion information. The holes generated during inverse motion compensation can be filled with the reference frames. The experimental results show that the proposed algorithm provides higher Bjontegaard-delta peak signal-to-noise ratios (BD-PSNRs) of 0.63 and 0.57 dB on an average compared with conventional H.264 and HEVC platforms, respectively.

The traffic load of cellular networks varies in both time and spatial domains, causing many base stations (BS) to be under-utilized. Assisted by cell zooming, dynamic BS sleep control is considered as an effective way to improve energy efficiency during low traffic hours. Therefore, how densely the BSs should be deployed with cell zooming and BS sleeping is an important issue. In this paper, we explore the energy-optimal cellular network planning problem with dynamic BS sleeping and cell zooming for the cases in which traffic is uniformly distributed in space but time-varying. To guarantee the quality of multi-class services, an approximation method based on Erlang formula is proposed. Extensive simulations under our predefined scenarios show that about half of energy consumption can be saved through dynamic BS sleeping and power control. Surprisingly, the energy-optimal BS density we obtained is larger than the one without considering BS sleeping. In other words, deploying more BSs may help to save energy if dynamic BS sleeping is executed.

In this research, we focus on how to track a target region that lies next to similar regions (e.g. a forearm and an upper arm) in zoom-in images. Many previous tracking methods express the target region (i.e. a part in a human body) with a single model such as an ellipse, a rectangle, and a deformable closed region. With the single model, however, it is difficult to track the target region in zoom-in images without confusing it and its neighboring similar regions (e.g. "a forearm and an upper arm" and "a small region in a torso and its neighboring regions") because they might have the same texture patterns and do not have the detectable border between them. In our method, a group of feature points in a target region is extracted and tracked as the model of the target. Small differences between the neighboring regions can be verified by focusing only on the feature points. In addition, (1) the stability of tracking is improved using particle filtering and (2) tracking robust to occlusions is realized by removing unreliable points using random sampling. Experimental results demonstrate the effectiveness of our method even when occlusions occur.

In this paper we deal with the problem of calibrating a rotating and zooming camera, without 3D pattern, whose internal calibration parameters change frame by frame. First, we theoretically show the existence of the calibration parameters up to an orthogonal transformation under the assumption that the skew of the camera is zero. Auto-calibration becomes possible by analyzing inter-image homographies which can be obtained from the matches in images of the same scene, or through direct nonlinear iteration. In general, at least four homographies are needed for auto-calibration. When we further assume that the aspect ratio is known and the principal point is fixed during the sequence then one homography yields camera parameters, and when the aspect ratio is assumed to be unknown with fixed principal point then two homographies are enough. In the case of a fixed principal point, we suggest a method for obtaining the calibration parameters by searching the space of the principal point. If this is not the case, then nonlinear iteration is applied. The algorithm is implemented and validated on several sets of synthetic data. Also experimental results for real images are given.

This paper describes an adaptive sensing system with tracking and zooming a moving object in the stable environment. Both the close contour matching technique and the effective determination of zoom ratio by fuzzy control are proposed for achieving the sensing system. First, the estimation of object feature parameters, 2-dimensional velocity and size, is based on close contour matching. The correspondence problem is solved with cross-correlation in projections extracted from object contours in the specialized difference images. In the stable environment, these contours matching, capable of eliminating occluded contours or random noises as well as background, works well without heavy-cost optical flow calculation. Next, in order to zoom the tracked object in accordance with the state of its shape or movement practically, fuzzy control is approached first. Three sets of input membership function--the confidence of object shape, the variance of object velocity, and the object size--are evaluated with the simplified implementation. The optimal focal length is achieved of not only desired size but safe tracking in combination with fuzzy rule matrix constituted of membership functions. Experimental results show that the proposed system is robust and valid for numerous kind of moving object in real scene with system period 1.85 sec.