Zhongke Wu

Active contour model combining region and edge information

A novel active contour model is proposed by combining region and edge information. Its level set formulation consists of the edge-related term, the region-based term and the regularization term. The edge-related term is derived from the image gradient, and facilitates the contours evolving into object boundaries. The region-based term is constructed using both local and global statistical information, and related to the direction and velocity of the contour propagation. The last term ensures stable evolution of the contours. Finally, a Gaussian convolution is used to regularize the level set function. In addition, a new quantitative metric named modified root mean squared error is defined, which can be used to evaluate the final contour more accurately. Experimental results show that the proposed method is efficient and robust, and can segment homogenous images and inhomogenous images with the initial contour being set freely.

A hierarchical dense deformable model for 3D face reconstruction from skull

Abstract3D face reconstruction from skull has been investigated deeply by computer scientists in the past two decades because it is important for identification. The dominant methods construct 3D face from the soft tissue thickness measured at a set of landmarks on skull. The quantity and position of the landmarks are very vital for 3D face reconstruction, but there is no uniform standard for the selection of the landmarks. Additionally, the acquirement of the landmarks on skull is difficult without manual assistance. In this paper, an automatic 3D face reconstruction method based on a hierarchical dense deformable model is proposed. To construct the model, the skull and face samples are acquired by CT scanner and represented as dense triangle mesh. Then a non-rigid dense mesh registration algorithm is presented to align all the samples in point-to-point correspondence. Based on the aligned samples, a global deformable model is constructed, and three local models are constructed from the segmented patches of the eye, nose and mouth. For a given skull, the globe and local deformable models are iteratively matched with it, and the reconstructed facial surface is obtained by fusing the globe and local reconstruction results. To validate the presented method, a measurement in the coefficient domain of a face deformable model is defined. The experimental results indicate that the proposed method has good performance for 3D face reconstruction from skull.

A novel skull registration based on global and local deformations for craniofacial reconstruction

Craniofacial reconstruction is important in forensic identification. It aims to estimate a facial appearance for human skeletal remains using the relationship between the soft tissue and the underlying bone structure. Various computerized methods have been developed in recent decades. An effective way is to deform a reference skull to the discovered skull, and then apply the same deformation to the skin associated with the reference skull to provide an approximate face for the discovered skull. For this method, the better the two skulls match each other, the more face-like the reconstructed skin surface will be. In this paper, we present a novel skull registration method that can match the two skulls closely, so as to improve the accuracy of the reconstruction. It combines both global and local deformations. A generic thin-plate spline (TPS)-based deformation, which is global, is applied first to roughly align the two skulls based on two groups of manually defined landmarks. Afterwards, the two skulls are largely matched, except some regions, on which some new landmarks are automatically marked. A compact support radial basis functions (CSRBF)-based deformation, which is local, will then be performed on these regions to adjust the initial alignment of the two skulls. Such adjustment can be repeatedly implemented until the two skulls have optimal alignment. In addition, all the skulls and face involved in the registration are represented by their single outer surfaces to facilitate the reconstruction procedure. The experiments demonstrate that our method can create a plausible face even when the reference skull is very different from the discovered skull. As a result, we can make full use of our database to provide multiple estimates for a principle components analysis (PCA) for the final reconstruction.

Skeleton Based Parametric Solid Models: Ball B-Spline Surfaces

This paper proposes a new skeleton based parametric representation of freeform shell-like solid objects - Ball B-Spline Surfaces (BBSSs) and their fundamental properties and algorithms. BBSSs are generalizations of ball B-Spline curve from one parameter variable to two parameter variables. BBSSs directly define objects in B- Spline form, unlike a procedure method like sweeping. BBSSs describe not only every point inside 3D solid objects, but also provides their center surface (skeleton). So the representation is more flexible for modeling, manipulation and deformation.

DBSC‐based animation enhanced with feature and motion

Disk B‐spline curve (DBSC) is previously proposed for drawing and animation. To generate inbetweens, linear interpolation is applied between points evenly taken in parametric domain of two DBSCs without incorporating characteristics of shape or motion, which results in distortion and unrealistic motion in animation. In this paper, more information in keyframes is extracted and utilized in inbetween generation. Points with high curvature are computed and corresponded between strokes in interpolation, which preserves features of strokes in animation. In addition, global motion of a character or its various components is estimated and interpolated as well, which retains the shapes during the motion. By applying the information to interpolation, the distortion is eliminated and smoother sequence of animation is achieved. Copyright

A novel statistical cerebrovascular segmentation algorithm with particle swarm optimization

Abstract We present an automatic statistical intensity-based approach to extract the 3D cerebrovascular structure from time-of flight (TOF) magnetic resonance angiography (MRA) data. We use the finite mixture model (FMM) to fit the intensity histogram of the brain image sequence, where the cerebral vascular structure is modeled by a Gaussian distribution function and the other low intensity tissues are modeled by Gaussian and Rayleigh distribution functions. To estimate the parameters of the FMM, we propose an improved particle swarm optimization (PSO) algorithm, which has a disturbing term in speeding updating the formula of PSO to ensure its convergence. We also use the ring shape topology of the particles neighborhood to improve the performance of the algorithm. Computational results on 34 test data show that the proposed method provides accurate segmentation, especially for those blood vessels of small sizes.

Craniofacial reconstruction based on multi-linear subspace analysis

Craniofacial reconstruction aims to estimate an individual’s facial appearance from its skull. It can be applied in many multimedia services such as forensic medicine, archaeology, face animation etc. In this paper, a statistical learning based method is proposed for 3D craniofacial reconstruction. In order to well represent the craniofacial shape variation and better utilize the relevance between different local regions, two tensor models are constructed for the skull and the face skin respectively, and multi-linear subspace analysis is used to extract the craniofacial subspace features. A partial least squares regression (PLSR) based mapping from skull subspace to skin subspace is established with the attributes such as age and BMI being considered. For an unknown skull, the 3D face skin is reconstructed using the learned mapping with the help of the skin tensor model. Compared with some other statistical learning based method in literature, the proposed method more directly and properly reflects the shape relationship between the skull and the face. In addition, the proposed method has little manual intervention. Experimental results show that the proposed method is valid.

A dense point-to-point alignment method for realistic 3D face morphing and animation

We present a new point matching method to overcome the dense point-to-point alignment of scanned 3D faces. Instead of using the rigid spatial transformation in the traditional iterative closest point (ICP) algorithm, we adopt the thin plate spline (TPS) transformation to model the deformation of different 3D faces. Because TPS is a non-rigid transformation with good smooth property, it is suitable for formulating the complex variety of human facial morphology. A closest point searching algorithm is proposed to keep one-to-one mapping, and to get good efficiency the point matching method is accelerated by a KD-tree method. Having constructed the dense point-to-point correspondence of 3D faces, we create 3D face morphing and animation by key-frames interpolation and obtain realistic results. Comparing with ICP algorithm and the optical flow method, the presented point matching method can achieve good matching accuracy and stability. The experiment results have shown that our method is efficient for dense point objects registration.

Artistic brushstroke representation and animation with disk b-spline curve

A novel modeling and representation approach for brushstroke and animation is proposed in this paper based on disk B-spline curve (DBSC). This method represents not only the 2D region of the stroke but also its centerline so that various attributes like scalar and vector field can be applied to the stroke. Given two or more animation drawings represented by DBSC, the intermediate frames can be automatically generated to form a smoother sequence, either linearly or non-linearly. Moreover, compared to other models like polygon and triangle mesh, DBSC allows more flexible manipulation of brushstroke. With smaller dataset, DBSC has advantages to store and transmit the model across the network thus may be suitable to online games and animation.

Feature- and region-based auto painting for 2D animation

This paper presents a novel approach to automating the entire coloring process in traditional cel animation production. A feature-based region-matching algorithm is proposed. It first matches a set of master frames to construct correspondences between regions in the master frames and extract a stable topology. The first frame of each scene is then colored based on the set of master frames. With the painted first frame and established stable topology, each region in subsequent frames is matched with regions in the previous frames and colored. Compared with other algorithms, our approach is able to handle bigger changes between frames and automatically color the first frame of each sequence.