Discovering the variations in human torso shape plays a key role in many design-oriented applications, such as suit designing. With recent advances in 3D surface imaging technologies, people can obtain 3D human torso data that provide more information than traditional measurements. However, how to find different human shapes from 3D torso data is still an open problem. In this paper, we propose to use spectral clustering approach on torso manifold to address this problem. We first represent high-dimensional torso data in a low-dimensional space using manifold learning algorithm. Then the spectral clustering method is performed to get several disjoint clusters. Experimental results show that the clusters discovered by our approach can describe the discrepancies in both genders and human shapes, and our approach achieves better performance than the compared clustering method.
A common limitation of laser line three-Dimensional (3D) scanners is the inability to scan objects with surfaces that are
either parallel to the laser line or that self-occlude. Filling in missing areas adds some unwanted inaccuracy to the 3D
model. Capturing the human head with a Cyberware PS Head Scanner is an example of obtaining a model where the
incomplete areas are difficult to fill accurately. The PS scanner uses a single vertical laser line to illuminate the head
and is unable to capture data at top of the head, where the line of sight is tangent to the surface, and under the chin, an
area occluded by the chin when the subject looks straight forward. The Cyberware PX Scanner was developed to obtain
this missing 3D head data. The PX scanner uses two cameras offset at different angles to provide a more detailed head
scan that captures surfaces missed by the PS scanner. The PX scanner cameras also use new technology to obtain color
maps that are of higher resolution than the PS Scanner. The two scanners were compared in terms of amount of surface
captured (surface area and volume) and the quality of head measurements when compared to direct measurements
obtained through standard anthropometry methods. Relative to the PS scanner, the PX head scans were more complete
and provided the full set of head measurements, but actual measurement values, when available from both scanners,
were about the same.
Traditionally, medical geneticists have employed visual inspection (anthroposcopy) to clinically evaluate dysmorphology. In the last 20 years, there has been an increasing trend towards quantitative assessment to render diagnosis of anomalies more objective and reliable. These methods have focused on direct anthropometry, using a combination of classical physical anthropology tools and new instruments tailor-made to describe craniofacial morphometry. These methods are painstaking and require that the patient remain still for extended periods of time. Most recently, semiautomated techniques (e.g., structured light scanning) have been developed to capture the geometry of the face in a matter of seconds. In this paper, we establish that direct anthropometry and structured light scanning yield reliable measurements, with remarkably high levels of inter-rater and intra-rater reliability, as well as validity (contrasting the two methods).
Automatic extraction of anatomic landmarks from three-dimensional (3-D) head scan data is a typical, also challenging application of 3-D image analysis. This paper explored approaches to automatically identify landmarks based on their geometric appearance in a 3-D data set. We investigated the geometric features of most important landmarks of the head/face, especially invariant surface characteristics such as mean and Gaussian curvature, and other external characteristics as well. Based on the analysis of these features, we define a number of methods and operators to locate each extractable landmark from 3-D scan data. Starting from nose, the process to locate face landmarks can be conducted in a structural way and we reduced the image analysis of each landmark to a local area. Ideally, the characteristic map derived from a 3-D digital image should deliver a meaningful image for analysis. However, due to noise and void in the data set, it is not unusual the characteristic map has to be post-processed or re-computed. A number of experiments is conducted to find the suitable computational technique and additional steps are taken to obtain a satisfied characteristic map.
The objects captured with three-dimensional scanners are, by themselves, of limited value. The real power of 3D scanning emerges as applications derive useful information from the point clouds. Extracting measurements from 3D human body scans is an important capability for those interested in clothing and equipment design, human factors evaluation, and web commerce, among other applications. In order to be practical, measurement extraction functions must be fast, accurate, and reliable. Automation is critical for processing the large numbers of scans envisioned by most developers. In this paper we report two functions for identifying feducial points (landmarks) on the human face. First, we used a template-matching approach where a predefined template of 34 face landmarks is matched to a head scan using a small subset of the template landmarks. Once the template is in place, interrogating local surface geometry refines landmark location. This approach allows us to locate a large number of landmarks quickly, and, more importantly, it allows us to place important but hard to locate landmarks. In our second approach, we used image-processing methods to locate a small blue dot that has been positioned on the face prior to scanning.
Most current 3-D head scanners cannot capture a complete surface of the head due to limitation in view. As a postprocessing aid, we developed an automated method for approximating the top of the head surface. The top-of-head surface is usually the largest void area in a 360-degree head scan such as these obtained with a Cyberware PS head scanner. In this paper, we describe a two-step B-spline curve/surface approximation process to reconstruct the top ofhead from raw data set.
KEYWORDS: Image segmentation, 3D scanning, 3D image processing, Data modeling, 3D modeling, Image processing algorithms and systems, Data acquisition, Whole body imaging, Medical imaging, Chest
This paper presents a segmentation algorithm for 3D whole body surface scan data. The algorithm is based on 2D projection of 3D data and has achieved good result in a number of limited surface shapes. The method has been successfully employed to extract the torso, arm, and leg segments of the human body.
KEYWORDS: 3D scanning, Natural surfaces, 3D image processing, Tissues, Head, Clouds, Personal protective equipment, Scanners, Tissue optics, Data centers
Surface area coverage is an important feature for evaluating the functionality of personal protective equipment and clothing. This paper present an approach for calculating surface area coverage of protective clothing by superimposing two 3D whole body scan images: a scan of a 'nude' human/mankind body and a scan of a clothed body. The basic approach is to align two scans and calculate the per vertex distance field between the two scanned surfaces. Because the clothed body has an extra surface layer relative to the nude scan, the distance field may be used to define covered or uncovered regions by setting a distance threshold based on the thickness of the clothing or equipment. This paper discusses the procedures required for estimating surface area coverage including data slicing, sorting, mesh generation and the computation of the distance field. Although the above method is straightforward to describe, some difficulties related to human body scanning had to be overcome in the practical application of the method. Some of these challenges included: 1) registration of two scan data sets with different shapes, 2) the frequency occurrence of void data, especially in the clothing scan; and 3) the clothing/equipment may cause tissue compression and deformation. This paper discusses these problems and our current solutions.
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