Development of ergonomic posture recognition technique based on 2D ordinary camera for construction hazard prevention through view-invariant features in 2D skeleton motion

Outdoor tasks operated by construction workers are physically demanding, requiring awkward postures leading to pain, injury, accident, or permanent disability. Ergonomic posture recognition (EPR) technique could be a novel solution for ergonomic hazard monitoring and assessment, yet non-intrusiveness and applicability in complex outdoor environment are always critical considerations for device selection in construction site. Thus, we choose RGB camera to capture skeleton motions, which is non-intrusive for workers compared with wearable sensors. It is also stable and widely used in an outdoor construction site considering various light conditions and complex working areas. This study aims to develop an ergonomic posture recognition technique based on 2D ordinary camera for construction hazard prevention through view-invariant features in 2D skeleton motion. Based on captured 2D skeleton motion samples in the test-run, view-invariant features as classifier inputs were extracted to ensure the learned classifier not sensitive to various camera viewpoints and distances to a worker. Three posture classifiers regarding human back, arms, and legs were employed to ensure three postures to be recognized simultaneously in one video frame. The average accuracies of three classifiers in 5-fold cross validation were as high as 95.0%, 96.5%, and 97.6%, respectively, and the overall accuracies tested by three new activities regarding ergonomic assessment scores captured from different camera heights and viewpoints were 89.2%, 88.3%, and 87.6%, respectively. The developed EPR-aided construction accident auto-prevention technique demonstrated robust accuracy to support on-site postural ergonomic assessment for construction workers’ safety and health assurance.     

Complete 3D Foot Scanning System Using 360 Degree Rotational and Translational Laser Triangulation Sensors

This paper proposes a new type of 3D foot scanning system using rotational and translational 3D scanning stages. Commercial 3D foot scanning systems (or scanners) mostly employ the laser triangulation method and three or more linear stages to scan the entire 3D shape of the foot. We introduce a new foot scanning method using only two laser-camera triangulation sensors. The proposed scanning system consists of a 360° rotational and a linear translational 3D sensors. The rotational sensor employs two line lasers with a vision camera to solve an occlusion problem of the rotational stage and acquires the 3D shape of the upper part of the foot. The translational sensor consists of a line laser and a vision camera and acquires the 3D shape of the foot sole. The performance of the proposed scanning technique is verified using plastic models and human feet. In average, about 0.5 mm reconstruction accuracy is obtained by the proposed technique.

     

3D Body Shapes Estimation from Dressed‐Human Silhouettes

Estimation of 3D body shapes from dressed‐human photos is an important but challenging problem in virtual fitting. We propose a novel automatic framework to efficiently estimate 3D body shapes under clothes. We construct a database of 3D naked and dressed body pairs, based on which we learn how to predict 3D positions of body landmarks (which further constrain a parametric human body model) automatically according to dressed‐human silhouettes. Critical vertices are selected on 3D registered human bodies as landmarks to represent body shapes, so as to avoid the time‐consuming vertices correspondences finding process for parametric body reconstruction. Our method can estimate 3D body shapes from dressed‐human silhouettes within 4 seconds, while the fastest method reported previously need 1 minute. In addition, our estimation error is within the size tolerance for clothing industry. We dress 6042 naked bodies with 3 sets of common clothes by physically based cloth simulation technique. To the best of our knowledge, We are the first to construct such a database containing 3D naked and dressed body pairs and our database may contribute to the areas of human body shapes estimation and cloth simulation.     

基于B样条插值函数的人脑MR图像非刚体配准方法

提出了一种B样条插值函数结合图像特征标记的人脑MR图像非刚体配准方法。图像的特征标记选取图像的内外部轮廓来描述,目标和源特征之间的对应关系通过距离图来自动获得。形变过程采用B样条曲面函数插值来完成。通过多级B样条插值方法,即先全局计算后局部细化的方法来逐步优化形变结果。实验表明,该算法在不失准确度的前提下,具备较快的计算速度。     

Modeling and animation of facial expressions based on B-Splines

This paper describes a technique for the automatic adaptation of a canonical facial model to data obtained by a 3D laser scanner. The facial model is a B-spline surface with 13×16 control points. We introduce a technique by which this canonical model is fit to the scanned data and that takes into consideration the requirements for the animation of facial expressions. The animation of facial expressions is based on the facial action coding system (FACS). Using B-splines in combination with FACS, we automatically create the impression of a moving skin. To increase the realism of the animation we map textural information onto the B-spline surface.     

Efficient reconstruction from non-uniform point sets

We propose a method for non-uniform reconstruction of 3D scalar data. Typically, radial basis functions, trigonometric polynomials or shift-invariant functions are used in the functional approximation of 3D data. We adopt a variational approach for the reconstruction and rendering of 3D data. The principle idea is based on data fitting via thin-plate splines. An approximation by B-splines offers more compact support for fast reconstruction. We adopt this method for large datasets by introducing a block-based reconstruction approach. This makes the method practical for large datasets. Our reconstruction will be smooth across blocks. We give reconstruction measurements as error estimations based on different parameter settings and also an insight on the computational effort. We show that the block size used in reconstruction has a negligible effect on the reconstruction error. Finally we show rendering results to emphasize the quality of this 3D reconstruction technique.     

Visual SLAM Based on Rigid-Body 3D Landmarks

In current visual SLAM methods, point-like landmarks (As in Filliat and Meyer (Cogn Syst Res 4(4):243–282, 2003), we use this expression to denote a landmark generated by a point or an object considered as punctual.) are used for representation on maps. As the observation of each point-like landmark gives only angular information about a bearing camera, a covariance matrix between point-like landmarks must be estimated in order to converge with a global scale estimation. However, as the computational complexity of covariance matrices scales in a quadratic way with the number of landmarks, the maximum number of landmarks that is possible to use is normally limited to a few hundred. In this paper, a visual SLAM system based on the use of what are called rigid-body 3D landmarks is proposed. A rigid-body 3D landmark represents the 6D pose of a rigid body in space (position and orientation), and its observation gives full-pose information about a bearing camera. Each rigid-body 3D landmark is created from a set of N point-like landmarks by collapsing 3N state components into seven state components plus a set of parameters that describe the shape of the landmark. Rigid-body 3D landmarks are represented and estimated using so-called point-quaternions, which are introduced here. By using rigid-body 3D landmarks, the computational time of an EKF-SLAM system can be reduced up to 5.5%, as the number of landmarks increases. The proposed visual SLAM system is validated in simulated and real video sequences (outdoor). The proposed methodology can be extended to any SLAM system based on the use of point-like landmarks, including those generated by laser measurement.