In the field of materials sciences such as studies on ceramics, semi-conducting material and metals, role of light elements is important, because it is one of mainly composing elements or determiner of character i. e. dopants. The light elements at high resolution have been observed by ultrahigh voltage electron microscopy or aberration corrected electron microscopy in Transmission Electron Microscopy (TEM), since the visualization of light requires highly resolving power. Recently, a high angle annular dark field (HAADF) scanning transmission electron microscopy (STEM) has become widely used in this field because of high-resolution capability and easily interpretable image contrast, which is roughly proportional to square of atomic number Z (Z 2 ). However, the HAADF image sometimes gives lack of light element because of excess contrast originated from Z 2 , when the specimen contains the light and heavy elements. The TEM bright field imaging gives an image contrast roughly proportional to the Z, when the specimen is thin enough to be able to apply the 'thin film approximation'. We have examined to apply an STEM annular bright field (ABF) imaging, which is equivalent to TEM hollow cone illumination imaging technique [1-2], to the oxide or nitride samples for simultaneous visualization of light and heavier elements. According to the article on hollow cone illumination in TEM [2], the contrast transfer in ABF expected to give better resolution than conventional BF STEM and to give non-oscillating contrast transfer, which gives easily-interpretable images unlike the BF STEM. This paper reports characteristics and the experimental result of the ABF imaging technique.Experiments were performed with a new 200 kV microscope (JEM-ARM200F) equipped with an annular bright field and dark field detectors as well as a spherical aberration correction system for STEM [3]. Figure 1 shows a scheme for our experiment. This experimental configuration enables us to perform a simultaneous acquisition of annular dark and bright field images. The convergent angle of incident beam is limited with the aperture in condenser lens system. The inner and outer acceptance angle for HAADF image is limited with the camera length and size of the HAADF detector. Those for ABF image is limited with the camera length, size of the ABF detector and the size of preventing disc placed above the bright field detector. Figures 2 (a), (b) and (c) show the HAADF, conventional BF and ABF images of SrTiO 3 (001). And the model of SrTiO 3 (001) is illustrated in Fig. 2 (d). The experimental parameters for the observation are listed as follows. Detecting angle for HAADF, BF and ABF were 68 -200 mrad, 0-22 mrad, 11-22 mrad, respectively. The probe current and size were 24 pA and 0.1 nm. If we focus on the site of oxygen, the oxygen is invisible in the HAADF image ( Fig. 2(a)) and slightly visible in the BF image ( Fig. 2(b)). While in the ABF image (Fig. 2(c)), the oxygen is clearly visible. Additional experiments of focal dependency were performed with the th...

On March 20, 2005, a large M JMA 7.0 earthquake occurred in the offshore area, west of Fukuoka prefecture, northern Kyushu, Japan. A series of joint observations were carried out by teams from several universities in Japan with the aim of investigating the aftershock activity. Six online telemetered and 17 offline recording seismic stations were installed on land around the aftershock area immediately followed the occurrence of the mainshock. Because aftershocks were located mainly in offshore regions, we also installed 11 ocean bottom seismometers (OBSs) just above the aftershock region and its vicinity in order to obtain accurate locations of hypocenters. The OBS observation was carried out from March 27 to April 13, 2005. We further conducted temporary GPS observations in which ten GPS receivers were deployed around the aftershock region. The aftershocks were mainly aligned along an approximately 25-km-long NW-SE trend, and the hypocenters of the main aftershock region were distributed on a nearly vertical plane at depths of 2-16 km. The mainshock was located near the central part of the main aftershock region at a depth of approximately 10 km. The largest aftershock of M JMA 5.8 occurred near the southeastern edge of the main aftershock region, and the aftershock region subsequently extended about 5 km in the SE direction as defined by secondary aftershock activity. Enlargement of the aftershock region did not occur after the peak in aftershock activity, and the aftershock activity gradually declined. The distribution of hypocenters and seismogenic stress as defined by aftershocks suggest that the 2005 West Off Fukuoka Prefecture Earthquake occurred on the fault that is the NW extension of the Kego fault, which extends NW-SE through the Fukuoka metropolitan area, and that the largest aftershock occurred at the northwestern tip of the Kego fault.

On March 20, 2005 the West off Fukuoka Prefecture earthquake (magnitude of 7.0 on the JMA scale) occurred in southeastern Japan. The earthquake fault was a left-lateral strike-slip having a nearly vertical fault plane and a strike in the WNW-ESE direction. The largest aftershock with a magnitude of 5.8 (JMA) followed 1 month later. To gain more detailed aftershock data, several teams from different Japanese universities jointly installed a number of temporary seismic stations and positioned Ocean Bottom Seismometers (OBSs) immediately above the focal area. Double-difference tomography was used to estimate the three-dimensional (3D) (Zhang and Thurber, 2003) velocity structures in and around the focal area based on the travel time data collected during seismic observations. The high-velocity regions estimated by the inversion are located on the edge of the aftershock area and on the shallow part of asperity, as inferred from the slip distribution. Conversely, the V p/V s ratio is not always as high as that found at the location of the asperity. This finding suggests that the construction of the medium is not uniformly elastic but complex, with different relations between elastic constants and strength.

Abstract. This paper describes a method of stereoscopic view generation by image-based rendering in wide outdoor environments. The stereoscopic view can be generated from an omnidirectional image sequence by a light field rendering approach which generates a novel view image from a set of images. The conventional methods of novel view generation have a problem such that the generated image is distorted because the image is composed of parts of several omnidirectional images captured at different points. To overcome this problem, we have to consider the distances between the novel viewpoint and observed real objects in the rendering process. In the proposed method, in order to reduce the image distortion, stereoscopic images are generated considering depth values estimated by dynamic programming (DP) matching using the images that are observed from different points and contain the same ray information in the real world. In experiments, stereoscopic images in wide outdoor environments are generated and displayed.

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This paper proposes a novel method for generating arbitrary stereoscopic view from multiple omnidirectional image sequences. Although conventional methods for arbitrary view generation with an image-based rendering approach can create binocular views, positions and directions of viewpoints for stereoscopic vision are limited to a small range. In this research, we attempt to generate arbitrary stereoscopic views from omnidirectional image sequences that are captured in various multiple paths. To generate a high-quality stereoscopic view from a number of images captured at various viewpoints, appropriate ray information needs to be selected. In this paper, appropriate ray information is selected from a number of omnidirectional images using a penalty function expressed as ray similarity. In experiments, we show the validity of this penalty function by generating stereoscopic view from multiple real image sequences.

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Traffic congestion detection systems help manage traffic in crowded cities by analyzing videos of vehicles. Existing systems largely depend on texture and motion features. Such systems face several challenges, including illumination changes caused by variations in weather conditions, complexity of scenes, vehicle occlusion, and the ambiguity of stopped vehicles. To overcome these issues, this paper proposes a rapid and reliable traffic congestion detection method based on the modeling of video dynamics using deep residual learning and motion trajectories. The proposed method efficiently uses both motion and deep texture features to overcome the limitations of existing methods. Unlike other methods that simply extract texture features from a single frame, we use an efficient representation learning method to capture the latent structures in traffic videos by modeling the evolution of texture features. This representation yields a noticeable improvement in detection results under various weather conditions. Regarding motion features, we propose an algorithm to distinguish stopped vehicles and background objects, whereas most existing motion-based approaches fail to address this issue. Both types of obtained features are used to construct an ensemble classification model based on the support vector machine algorithm. Two benchmark datasets are considered to demonstrate the robustness of the proposed method: the UCSD dataset and NU1 video dataset. The proposed method achieves competitive results (97.64% accuracy) when compared to state-ofthe-art methods.