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This paper presents a general framework that aims to address the task of segmenting three-dimensional (3-D) scan data representing the human form into subsets which correspond to functional human body parts. Such a task is challenging due to the articulated and deformable nature of the human body. A salient feature of this framework is that it is able to(More)
OBJECTIVE The aim of this study was to characterize the soft tissue facial features of infants without cleft and to report on the correlation between these with weight, length, and head circumference. DESIGN This was a prospective study using a noninvasive three-dimensional (3D) stereophotogrammetry (C3D) system to capture the images of the participants.(More)
— We present a visually guided, dual-arm, industrial robot system that is capable of autonomously flattening garments by means of a novel visual perception pipeline that fully interprets high-quality RGB-D images of the clothing scene based on an active stereo robot head. A segmented clothing range map is B-Spline smoothed prior to being parsed by means of(More)
Many 3-dimensional (3D) techniques have been utilized to register and analyze the face in 3 dimensions, but each system has its own merits and disadvantages. C3D is a relatively new 3D imaging system that was developed to capture the 3D geometry of the face. Landmark identification on 3D facial models is facilitated by a software-based facial analysis tool(More)
This paper presents an improved method to construct dense correspondences for 3D facial analysis, which are capable of providing a full 3D description of a surface and extending the conventional landmark-based approaches. Based on the technique of elastic deformation, the dense correspondences are established by mapping a generic model onto the 3D surface(More)
A stereo imaging system with six high-resolution cameras (3032 × 2028 pixels) and three flash units was developed to capture the 3D shapes of live pigs. The cameras were arranged in three stereo pods, which captured the side, top and rear views of each pig. The image resolution was 0.4 mm per pixel on the surface of the pig. The system was used to capture(More)