6023163
Automatic 3D model reconstruction using voxel coding and pose integration
Automatic reconstruction of a complete 3D model of a complex object is presented. The complete 3D model is reconstructed by integrating two 3D models which are reconstructed from different poses of the object. For each pose of the object, a 3D model is reconstructed by combining stereo image analysis, shape from silhouettes, and a volumetric integration technique. Stereo image analysis and shape from silhouettes techniques complement each other to reconstruct an accurate and noise-resistant 3D model. For a reliable volumetric integration of multiple partial shapes, a voxel coding technique is introduced. The voxel coding technique facilitates a selection of consistent partial shapes for shape integration. In order to reconstruct all visible surfaces of a complex object with concavities and holes, two 3D models from different poses of the object are reconstructed and integrated to obtain the complete 3D model. A voxel coding technique is again used during pose integration. Experimental results on a real object demonstrate that our approach has advantages and is effective.
6978005
Pose estimation and integration for complete 3D model reconstruction
An automatic 3D model reconstruction technique is presented to acquire complete 3D models of real objects. The technique is based on novel approaches to pose estimation and integration. Two different poses of an object are used because a single pose often hides some surfaces from a range sensor. The presence of hidden surfaces makes the 3D model reconstructed from any single pose a partial model. Two such partial 3D models are reconstructed for two different poses of the object using a multi-view 3D modeling technique. The two partial 3D models are then registered. Coarse registration is facilitated by a novel pose estimation technique between two models. The pose is estimated by matching a stable tangent plane (STP) of each pose model with the base tangent plane (BTP) which is invariant for a vision system. The partial models are then integrated to a complete 3D model based on voxel classification defined in multi-view integration. Texture mapping is done to obtain a photo-realistic reconstruction of the object.
34356748
Model-Based Human Motion Capture from Monocular Video Sequences
Generating motion and capturing motion of an articulated body for computer animation is an expensive and time-consuming task. Conventionally, animators manually generate intermediate frames between key frames, but this task is very labor-intensive. This paper presents a model-based singularity-free automatic-initialization approach to capturing human motion from widely-available, static background monocular video sequences. A 3D human body model is built and projected on a 2D projection plane to find the best fit with the foreground image silhouette. We convert the human motion capture problem into two types of parameter optimization problems: static optimization and dynamic optimization. First, we determine each model body configuration using static optimizations for every input image. Then, to obtain better description of motion, the results from all static optimizations are fed into a dynamic optimization process where the entire sequence of motion is considered for the user-specified motion. The user-specified motion is defined by the user and the final form of the motion they want. A cost function for static optimization is used to estimate the degree of overlapping between the foreground input image silhouette and a projected 3D model body silhouette. The overlapping is computed using computational geometry by converting a set of pixels from the image domain to a polygon in the real projection plane domain. A cost function for dynamic optimization is the user-specified motion based on the static optimization results as well as image fitting. Our method is used to capture various human motions: walking, pushing, kicking, and handshaking.
195862521
The wearable motherboard: a framework for personalized mobile information processing (PMIP)
Textiles and computing share a synergistic relationship, which is being harnessed to create a new paradigm in personalized mobile information processing (PMIP). In this paper, we provide an overview of this "interconnection" between the two fields and present the vision for "E-Textiles," which represents the convergence of the two fields. We discuss the role of the Georgia Tech Wearable Motherboard in pioneering this paradigm of "fabric is the computer" and serving as a framework for PMIP. Finally, recent research in this area resulting in the realization of a "computational fabric network" is discussed.
16000094
On-line motion blending for real-time locomotion generation
In this paper, we present an integrated framework of on-line motion blending for locomotion generation. We first provide a novel scheme for incremental timewarping, which always guarantees that the time goes forward. Combining the idea of motion blending with that of posture rearrangement, we introduce a motion transition graph to address on-line motion blending and transition simultaneously. Guided by a stream of motion specifications, our motion synthesis scheme moves from node to node in an on-line manner while blending a motion at a node and generating a transition motion at an edge. For smooth on-line motion transition, we also attach a set of example transition motions to an edge. To represent similar postures consistently, we exploit the inter-frame coherency embedded in the input motion specification. Finally, we provide a comprehensive solution to on-line motion retargeting by integrating existing techniques. Copyright © 2004 John Wiley & Sons, Ltd.
17481727
Strain analysis and visualization: left ventricle of a heart
Abstract Clinical utility of computational models is crucial in the applications of medical data visualization. Previously we have developed a new class of volumetric models whose parameters are functions in conjunction with physically based deformable modeling framework, and have applied the technique to estimate the left ventricular (LV) wall motion. We have successfully showed that the model parameter functions characterize the LV motion of normal and abnormal states and that no further non-trivial post-processing is required for anatomically meaningful interpretation. In an effort to evaluate the LV model, this paper presents a method and results from a strain analysis based on the nodal displacements of the deformable LV model. Furthermore, in order to visualize the local quantities on the volumetric model for an effective analysis, we also developed a methodology to assist in assessing the cardiac function utilizing principal strains, Von-Mises’ yield criteria, and a smoothing filter. Each strain tensor component was in the range of values observed in other reported studies. The application of a smoothing filter on the model improved in visualizing the overall trend of each strain variation. With our platform for a comprehensive strain analysis, we have augmented a clinical utility to the deformable models with parameter functions.
9401551
Sink-to-sensors reliability in sensor networks
The issue of relability, thus far, has not been addressed throughly to any extent in sensor networking research. To the best of our knowledge the first such work that focused on some aspect of reliable communication in sensor networks appeared only in [1]. In this paper, we focus on the problem of communication reliability from the sink to the sensors in a static network.