Fu-Che Wu

Skeleton extraction of 3D objects with radial basis functions

A skeleton is a lower dimensional shape description of an object. The requirements of a skeleton differ with applications. For example, object recognition requires skeletons with primitive shape features to make similarity comparison. On the other hand, surface reconstruction needs skeletons, which contain detailed geometry information to reduce the approximation error in the reconstruction process. Whereas many previous works are concerned about skeleton extraction, most of these methods are sensitive to noise, time consuming, or restricted to specific 3D models. A practical approach for extracting skeletons from general 3D models using radial basis functions (RBFs) is proposed. A skeleton generated with this approach conforms more to the human perception. Given a 3D polygonal model, the vertices are regarded as centers for RBF level set construction. Next, a gradient descent algorithm is applied to each vertex to locate the local maxima in the RBF; the gradient is calculated directly from the partial derivatives of the RBF. Finally, with the inherited connectivity from the original model, local maximum pairs are connected with links driven by the active contour model. The skeletonization process is completed when the potential energy of these links is minimized.

Cubical Marching Squares: Adaptive Feature Preserving Surface Extraction from Volume Data

In this paper, we present a new method for surface extraction from volume data which preserves sharp features, maintains consistent topology and generates surface adaptively without crack patching. Our approach is based on the marching cubes algorithm, a popular method to convert volumetric data to polygonal meshes. The original marching cubes algorithm suffers from problems of topological inconsistency, cracks in adaptive resolution and inability to preserve sharp features. Most of marching cubes variants only focus on one or some of these problems. Although these techniques could be combined to solve these problems altogether, such a combination might not be straightforward. Moreover, some feature-preserving variants introduce an additional problem, inter-cell dependency. Our method provides a relatively simple and easy-to-implement solution to all these problems by converting 3D marching cubes into 2D cubical marching squares, resolving topology ambiguity with sharp features and eliminating inter-cell dependency by sampling face sharp features. We compare our algorithm with other marching cubes variants and demonstrate its effectiveness on various applications.

Domain connected graph: the skeleton of a closed 3D shape for animation

In previous research, three main approaches have been employed to solve the skeleton extraction problem: medial axis transform (MAT), generalized potential field and decomposition-based methods. These three approaches have been formulated using three different concepts, namely surface variation, inside energy distribution, and the connectivity of parts. By combining the above mentioned concepts, this paper creates a concise structure to represent the control skeleton of an arbitrary object.First, an algorithm is proposed to detect the end, connection and joint points of an arbitrary 3D object. These three points comprise the skeleton, and are the most important to consider when describing it. In order to maintain the stability of the point extraction algorithm, a prong-feature detection technique and a level iso-surfaces function-based on the repulsive force field was employed. A neighborhood relationship inherited from the surface able to describe the connection relationship of these positions was then defined. Based on this relationship, the skeleton was finally constructed and named domain connected graph (DCG). The DCG not only preserves the topology information of a 3D object, but is also less sensitive than MAT to the perturbation of shapes. Moreover, from the results of complicated 3D models, consisting of thousands of polygons, it is evident that the DCG conforms to human perception.

Automatic animation skeleton using repulsive force field

A method is proposed in this paper to automatically generate the animation skeleton of a model such that the model can be manipulated according to the skeleton. With our method, users can construct the skeleton in a short time, and bring a static model both dynamic and alive. The primary steps of our method are finding skeleton joints, connecting the joints to form an animation skeleton, and binding skin vertices to the skeleton. Initially, a repulsive force field is constructed inside a given model, and a set of points with local minimal force magnitude are found based on the force field. Then, a modified thinning algorithm is applied to generate an initial skeleton, which is further refined to become the final result. When the skeleton construction completes, skin vertices are anchored to the skeleton joints according to the distances between the vertices and joints. In order to build the repulsive force field, hundreds of rays are shot radially from positions inside the model, and it leads to that the force field computation takes most of the execution time. Therefore, an octree structure is used to accelerate this process. Currently, the skeleton generated from a typical 3D model with 1000 to 10000 polygons takes less than 2 minutes on a Intel Pentium 4 2.4 GHz PC.

Automatic feature extraction and face synthesis in facial image coding

A real-time method that automatically extracts facial feature points is proposed. Based upon this technique, the authors also present a procedure to encode facial images efficiently. For clarity the proposed method is divided into an analysis part and synthesis part. The analysis part includes face tracking, feature extraction, and pose estimation. All of these procedures are applicable for live video applications. In the synthesis part, a new face image is synthesized using one base image and a small number of feature images. The current performance of feature extraction is about 8 frames per second on a Pentinum II 233 MHz PC with an image size 320/spl times/240.A real-time method that automatically extracts facial feature points is proposed. Based upon this technique, the authors also present a procedure to encode facial images efficiently. For clarity the proposed method is divided into an analysis part and synthesis part. The analysis part includes face tracking, feature extraction, and pose estimation. All of these procedures are applicable for live video applications. In the synthesis part, a new face image is synthesized using one base image and a small number of feature images. The current performance of feature extraction is about 8 frames per second on a Pentinum II 233 MHz PC with an image size 320/spl times/240.

Real-time 3-D head motion estimation in facial image coding

A simple procedure that uses only three feature points to infer 3D head motion from consecutive video frames is presented. In this procedure, a feature triangle formed using the three feature points is automatically calibrated, and an iterative method that simulates steepest descent method is applied to estimate ones head motion. A prediction algorithm is adopted in occasional cases where the estimated result is not acceptable. This procedure has been applied to live video with an update rate of 7 frames/sec (250 frames/sec without feature extraction) on a Pentium-II 233 MHz PC, independent of cameras and users.

Prong features detection of a 3D model based on the watershed algorithm: Copyright restrictions prevent ACM from providing the full text for this work.

In this paper, a simple and robust prong features detection algorithm is proposed. A prong feature is an assisting feature that can be used in many applications. For instance, it can be used to identify a model that consists of several prong parts for model decomposition. It represents a useful feature for skeleton extraction as well as a comparable feature for object matching. In addition, it could also be a fast alignment feature for model alignment and morphing. Furthermore, it is an invariant feature for mesh simplification.