This paper proposes a new framework to produce humanoid animations for simulating human tasks. Natural working movements are generated via management of motion capture data with our simulation package. An extensible middleware controls reactive human behaviors, and all processes of simulation in a cyber factory are controlled through XML documents including motions, scene objects, and behaviors. This package displays simulation using Web3D technology and X3D specifications which can supply a common interface for customizing cyberworlds.

This paper proposes an automatic system which can perform the entire diagnostic process from the extraction of the liver to the recognition of a tumor. In particular, the proposed technique uses shape information to identify and recognize a lesion adjacent to the border of the liver, which can otherwise be missed. Because such an area is concave like a bay, morphological operations can be used to find the bay. In addition, since the intensity of a lesion can vary greatly according to the patient and the slice taken, a decision on the threshold for extraction is not easy. Accordingly, the proposed method extracts the lesion by means of a Fuzzy c-Means clustering technique, which can determine the threshold regardless of a changing intensity. Furthermore, in order to decrease any erroneous diagnoses, the proposed system performs a 3-D consistency check based on three-dimensional information that a lesion mass cannot appear in a single slice independently. Based on experimental results, these processes produced a high recognition rate above 91%.

This paper proposes an automatic method for reconstructing a realistic 3D facial image from CT (computer tomography) and three color photographs: front, left and right views, which can be linked easily with the underlying bone and soft tissue models. This work is the first part of our final goal, "the prediction of patient's facial appearance after cancer surgery" such as removal of a part of bone or soft tissues. The 3D facial surface derived from CT by the marching cubes algorithm is obviously colorless. Our task is to add the color texture of the same patient actually taken with a digital camera to the colorless 3D surface. To do this it needs an accurate registration between the 3D facial image and the color photograph. Our approach is to set up a virtual camera around the 3D facial surface to register the virtual camera images with the corresponding color photographs by automatically adjusting seven parameters of the virtual camera. The camera parameters consists of three rotations, three translations and one scale factor. The registration algorithm has been developed based upon Besl and McKay's iterative closest point (ICP) algorithm.

A method for reconstructing realistic 3D human faces from computer tomography images and color photographs is proposed in this paper. This can be linked easily with the underlying bone and soft tissue models. An iteration algorithm has been developed for automatically estimating the virtual camera parameters to match the projected 3D CT image with 2D color photographs using known point correspondence. An approach has been proposed to select landmarks using a mouse with minimum error. Six landmarks from each image have been selected for front face matching and five for each side face matching.