Abstract
As the demands for customization increase, more detailed understanding and practical use of the size and shape information of human body becomes more and more important. Over the past decade, the 3D scanning technology has opened new opportunities for collecting body sizes and shapes, and related research topics are thus of great concern.In order to work with 3D scanning data efficiently, this study proposes a method for automated landmarking. After segmentation and initial searches, four algorithms can be employed for locating anatomical landmarks from 3D scanning data, including silhouette analysis, minimum circumference determination, gray-scale analysis, and human-body contour plots. And the method has been proved to be accurate and precise while comparing with traditional palpation works by testing on 189 human subjects.Based on the results of landmarking, 104 dimensions of the human body can be collected by using approximation methods. A two-stage evaluation was conducted for accessing the performance of scan-derived measurments in terms of accuracy and precision. By scanning 263 human subjects, the precision is high whereas the accuracy is not yet acceptable. Subsequently, by using a mannequin to eliminate the variation caused by human subject, both the accuracy and precision can be improved to meet the expectation. Thus, related counter-measures are critical and beneficial. Further, the effects of arm posture and subject gender were also investigated. It is suggested to adopt the arm posture with palms facing backward to assure higher accuracy and better image quality. Besides, the accuracy tends to be higher for males, and the possible causes should be considered for further improvements.In addition to size information, the torso shape are also obtained with four shape descriptors, including occupancy, contour variation, convex hull-based contour appearance, and ellipse-based contour appearance. The proposed shape descriptors have been evaluated and were found to be independent of scanning postures. By conducting factor analysis, the key descriptors can be identified to characterize the shape variation in chest, waist, and hip. Further, by performing clustering analysis, the shape clusters can be determined by considering multiple shape descriptors. Numerous applications can be realized, such as digital human modeling and apparel design.