In the field of computer vision and computer graphics, Image-Based-Rendering (IBR) methods are often used to synthesize images from real scene. The image synthesis by IBR requires dense correct matching points in the images. However, IBR does not require 3D geometry reconstruction or camera calibration in Euclidean geometry. On the other hand, 3D reconstructed model can easily point out the occlusion in images. In this paper, we propose an approach to reconstruct 3D shape in a voxel space, which is named Projective Voxel Space (PVS). Since PVS is defined by projective geometry, it requires only weak calibration. PVS is determined by rectifications of the epipolar lines in three images. Three rectified images are orthogonal projected images of a scene in PVS, so processing about image projection is easy in PVS. In both PVS and Euclidean geometry, a point in an image is on a projection from a point on a surface of the object in the scene. Then the other image might have a correct matching point without occlusion, or no matching point because of occlusion. This is a kind of restriction about searching matching points or surface of the object. Taking advantage of simplicity of projection in PVS, the correlation values of points in images are computed, and the values are iteratively refined using the restriction described above. Finally, the shapes of the objects in the scene are acquired in PVS. The reconstructed shape in PVS does not have similarity to 3D shape in Euclidean geometry. However, it denotes consistent matching points in three images, and also indicates the existence of occluded points. Therefore, the reconstructed shape in PVS is sufficient for image synthesis by IBR.

Recently, it becomes popular to synthesize new viewpoint images based on some sampled viewpoint images of real scene using technique of computer vision. 3D shape reconstruction in Euclidean space is not necessarily required, but information of dense matching points is basically enough to synthesize new viewpoint images. In this paper, we propose a new method for 3D reconstruction from three cameras based on projective geometry. In the proposed method, three input camera images are rectified based on projective geometry, so that the vertical and horizontal directions can be completely aligned with the epipolar planes between the cameras. This rectification provides Projective Voxel Space (PVS), in which the three axes are aligned with the directions of camera projection. Such alignment simplifies the procedure for projection between the 3D space and the image planes in PVS. Taking this advantage of PVS, silhouettes of the objects are projected into PVS, so that the searching area of matching points can be reduced. The consistency of color value between the images is also evaluated for final determination of the matching point. The finally acquired matching points in the proposed method are described as the surface of the objects in PVS. The acquired surface of the objects in PVS also includes knowledge about occlusion. Finally, images from new viewpoints can be synthesized from the matching points and occlusions. Although the proposed method requires only weak calibration, plausible occlusions are also synthesized in the images. In the experiments, images of virtual viewpoints, which were set among three cameras, are synthesized from three real images.

A transition between an NRD guide, suitable for construction of high performance millimeter-wave integrated circuits, and a microstrip line, being used to mount semiconductor devices such as HEMT, HBT, and MMIC, was developed at 60 GHz. The main emphasis was placed on the manner of field matching between the NRD guide and the microstrip line. We propose adoption of this a new transition structure employing a vertical strip line, which can be easily coupled to the NRD guide, and a coaxial line connected to the microstrip line. Moreover, we applied a packaging structure with a choke circuit for the microstrip line to prevent undesired leakage between the NRD guide and the microstrip line. The insertion loss of the fabricated transition was measured to be less than 0.5 dB in the bandwidth of 3 GHz at a center frequency of 60.5 GHz. The transition was applied to MMIC amplifier integration in the NRD guide at 60 GHz. The forward and reverse gains were measured to be 15 dB and -20 dB, respectively, at 60 GHz.

Recently, researches and developments for measuring and modeling of the human body have been receiving much attention. Our aim is to reconstruct an accurate shape of a human foot from multiple camera images, which can capture dynamic behavior of the object. In this paper, a foot-shape database is used for accurate reconstruction of human foot. By using Principal Component Analysis, the foot shape can be represented with new meaningful variables. The dimensionality of the data is also reduced. Thus, the shape of object can be recovered efficiently, even though the object is partially occluded in some input views. To demonstrate the proposed method, two kinds of experiments are presented: reconstruction of human foot in a virtual reality environment with CG multi-camera images, and in real world with eight CCD cameras. In the experiments, the reconstructed shape error with our method is around 2 mm in average, while the error is more than 4 mm with conventional volume intersection method.

A mode transformer between the NRD guide and the vertical strip line was developed and applied to the right angle corner, T-junction, and 3-port junction at 60 GHz. Emphasis was placed on a fully CAD-based design procedure by using an electromagnetic field simulator. Agreement between the simulated and measured performances of the junction circuit was obtained, and thus the validity of the design procedure was confirmed. A well-balanced transmission coefficient of the 3-port junction, found to be 4 0.5 dB, was observed in the bandwidth of 2 GHz around a center frequency of 60 GHz.