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Due to increasing attraction of motion capture systems technology and the usage of captured data in wide range of research-oriented applications, a framework has developed as an improved version of MOCAP TOOLBOX in Matlab platform. Firstly, we have introduced a faithful script to deal with public motion capture data, which will be friendly for us. Various functions through dynamic programming, by using the Body Segment Parameters (BSP) are edited and they configured the position of markers according to data. It is used to visualize and refine without the MLS view and the C3D editor software. It has opened a valuable way of sensor data in many research aspects as gait movements, marker analysis, compression and motion pattern, bioinformatics, and animation. As a result, performed on CMU and ACCAD public mocap data, and achieved higher corrected configuration scheme of 3D markers when compared with the prior art, especially for C3D file. Another distinction of this work is that it handles the extra markers distortion, and provides the meaningful way to use captured data.

In a recent publication on the voxelization of rational curves (Comput. Graphics 27(1) (2003) 83), a difference bound is used to maximize the parameter step, thus speeding up the voxelization. In fact, evaluation of difference bounds has found wide applications in computations of parametric curves and surfaces, as well as in error estimation, in general. While any correctly derived bound can be used for these purposes, a larger bound greatly improves computational efficiency. To respond to the strong interest shown by the graphics community, this short paper proposes very large difference bounds for rational Bezier curves and surfaces. The focus is on the derivation and mathematical proof of the bound evaluation, in the form of two theorems.

Craniofacial reconstruction is to estimate an individual’s face model from its skull. It has a widespread application in forensic medicine, archeology, medical cosmetic surgery, and so forth. However, little attention is paid to the evaluation of craniofacial reconstruction. This paper proposes an objective method to evaluate globally and locally the reconstructed craniofacial faces based on the geodesic network. Firstly, the geodesic networks of the reconstructed craniofacial face and the original face are built, respectively, by geodesics and isogeodesics, whose intersections are network vertices. Then, the absolute value of the correlation coefficient of the features of all corresponding geodesic network vertices between two models is taken as the holistic similarity, where the weighted average of the shape index values in a neighborhood is defined as the feature of each network vertex. Moreover, the geodesic network vertices of each model are divided into six subareas, that is, forehead, eyes, nose, mouth, cheeks, and chin, and the local similarity is measured for each subarea. Experiments using 100 pairs of reconstructed craniofacial faces and their corresponding original faces show that the evaluation by our method is roughly consistent with the subjective evaluation derived from thirty-five persons in five groups.

Way of walking is a spatio-temporal phenomenon that typifies the motion characteristics of human. In this paper, we propose a human gender identification method based on the outdoor surveillance of human gait using statistical techniques and geometrical function applied to three-dimensional (3D) joint movement data. The statistical techniques are used to define the features of the joint of the human gait, and the geometrical function applied for gender recognition. Our proposed scheme is based on extraction of the concern joint (hip joint) data that provides plentiful information for gender recognition. Here the rotation angle data of a hip joint were computed from the Biovision Hierarchical data (BVH file). The use of BVH file for human gender recognition is a novel feature of our work. The results indicated that the proposed approach is highly reliable for gender recognition. DOI: http://dx.doi.org/10.11591/telkomnika.v11i6.2274 Full Text: PDF

Similarity evaluation of 3D face is the core issue in 3D face recognition. The article puts forwards a geodesic stripes based evaluation method which realizes assessment from global face to local parts. It can be used to make effective and comprehensive evaluation in many fields such as 3D face reconstruction, forensic science, archaeology etc. First, simplify each 3D face with a series of geodesic stripes and calculate distribution vector between each pair of stripes, which reflects 3D space distribution feature. Then through feature extraction on entire face, we get a distribution matrix which consists of all distribution vectors. The similarity between two distribution matrices directly shows the global similarity between two faces. We also extract geodesic stripes feature on local organs like mouth eye and nose to make a more accurate evaluation. The experimental results on SHREC2008 3D face database further testify that the hierarchical evaluation method is available and consistent with the subjective evaluation.

International audience; In this paper, a system for re-assembling broken objects is discussed. It includes feature contours extraction, matching and re-assembling of 3D complementary objects, etc. These results are applicable to the restoration of culture relics through computer techniques. A computer assisted restoration system of culture relics is developed, which has been applied to the restoration of Terra Cotta Warriors and Horses of Qin dynasty.

Technology plays an important role in creation of arts, animation and game. The geometric model proposed here is a novel and advanced modeling and animation technology based on Disk and Ball B-Spline (DBBS).

BackgroundIn recent years, cerebrovascular disease has been the leading cause of death and adult disability in the world. This study describes an efficient approach to detect cerebrovascular disease. ObjectiveIn order to improve cerebrovascular treatment, prevention, and care, an automatic cerebrovascular disease detection eHealth platform is designed and studied. MethodsWe designed an automatic eHealth platform for cerebrovascular disease detection with a four-level architecture: object control layer, data transmission layer, service supporting layer, and application service layer. The platform has eight main functions: cerebrovascular database management, preprocessing of cerebral image data, image viewing and adjustment model, image cropping compression and measurement, cerebrovascular segmentation, 3-dimensional cerebrovascular reconstruction, cerebrovascular rendering, cerebrovascular virtual endoscope, and automatic detection. Several key technologies were employed for the implementation of the platform. The anisotropic diffusion model was used to reduce the noise. Statistics segmentation with Gaussian-Markov random field model (G-MRF) and Stochastic Estimation Maximization (SEM) parameter estimation method were used to realize the cerebrovascular segmentation. Ball B-Spline curve was proposed to model the cerebral blood vessels. Compute unified device architecture (CUDA) based on ray-casting volume rendering presented by curvature enhancement and boundary enhancement were used to realize the volume rendering model. We implemented the platform with a network client and mobile phone client to fit different users. ResultsThe implemented platform is running on a common personal computer. Experiments on 32 patients’ brain computed tomography data or brain magnetic resonance imaging data stored in the system verified the feasibility and validity of each model we proposed. The platform is partly used in the cranial nerve surgery of the First Hospital Affiliated to the General Hospital of People's Liberation Army and radiology of Beijing Navy General Hospital. At the same time it also gets some applications in medical imaging specialty teaching of Tianjin Medical University. The application results have also been validated by our neurosurgeon and radiologist. ConclusionsThe platform appears beneficial in diagnosis of the cerebrovascular disease. The long-term benefits and additional applications of this technology warrant further study. The research built a diagnosis and treatment platform of the human tissue with complex geometry and topology such as brain vessel based on the Internet of things.