Volumetric segmentation using Weibull E-SD fields

This paper presents a coarse-grain approach for segmentation of objects with gray levels appearing in volume data. The input data is on a 3D structured grid of vertices v(i. j. k), each associated with a scalar value. In this paper, we consider a voxel as a /spl kappa/ /spl times/ /spl kappa/ /spl times/ /spl kappa/ cube and each voxel is assigned two values: expectancy and standard deviation (E-SD). We use the Weibull noise index to estimate the noise in a voxel and to obtain more precise E-SD values for each voxel. We plot the frequency of voxels which have the same E-SD, then 3D segmentation based on the Weibull E-SD field is presented. Our test bed includes synthetic data as well as real volume data from a confocal laser scanning microscope (CLSM). Analysis of these data all show distinct and defining regions in their E-SD fields. Under the guide of the E-SD field, we can efficiently segment the objects embedded in real and simulated 3D data.     

Articulated Billboards for Video‐based Rendering

We present a novel representation and rendering method for free‐viewpoint video of human characters based on multiple input video streams. The basic idea is to approximate the articulated 3D shape of the human body using a subdivision into textured billboards along the skeleton structure. Billboards are clustered to fans such that each skeleton bone contains one billboard per source camera. We call this representation articulated billboards. In the paper we describe a semi‐automatic, data‐driven algorithm to construct and render this representation, which robustly handles even challenging acquisition scenarios characterized by sparse camera positioning, inaccurate camera calibration, low video resolution, or occlusions in the scene. First, for each input view, a 2D pose estimation based on image silhouettes, motion capture data, and temporal video coherence is used to create a segmentation mask for each body part. Then, from the 2D poses and the segmentation, the actual articulated billboard model is constructed by a 3D joint optimization and compensation for camera calibration errors. The rendering method includes a novel way of blending the textural contributions of each billboard and features an adaptive seam correction to eliminate visible discontinuities between adjacent billboards textures. Our articulated billboards do not only minimize ghosting artifacts known from conventional billboard rendering, but also alleviate restrictions to the setup and sensitivities to errors of more complex 3D representations and multiview reconstruction techniques. Our results demonstrate the flexibility and the robustness of our approach with high quality free‐viewpoint video generated from broadcast footage of challenging, uncontrolled environments.     

三角网格模型体素特征分割

针对现有机械制造领域网格模型分割结果缺少工程含义的现状,提出了一种三角网格模型体素特征分割方法。首先在对三角网格模型分割的基础上,对由网格分割得到的每个子网格进行曲面类型识别,然后在基本体素及典型结构显著特征表示的基础上,把识别出的曲面集合与基本体素及典型结构进行匹配,从而将分割结果分类为自由曲面、基本体素和复杂体素,实现具有工程含义的体素特征分割。该方法可以降低模型重构的难度,加快模型重构的速度。     

A model-based approach for estimating human 3D poses in static images

Estimating human body poses in static images is important for many image understanding applications including semantic content extraction and image database query and retrieval. This problem is challenging due to the presence of clutter in the image, ambiguities in image observation, unknown human image boundary, and high-dimensional state space due to the complex articulated structure of the human body. Human pose estimation can be made more robust by integrating the detection of body components such as face and limbs, with the highly constrained structure of the articulated body. In this paper, a data-driven approach based on Markov chain Monte Carlo (DD-MCMC) is used, where component detection results generate state proposals for 3D pose estimation. To translate these observations into pose hypotheses, we introduce the use of "proposal maps," an efficient way of consolidating the evidence and generating 3D pose candidates during the MCMC search. Experimental results on a set of test images show that the method is able to estimate the human pose in static images of real scenes.     

快速有效的半自动组织分割技术

针对医学可视化和虚拟内窥镜技术中的组织分割问题，提出边界模型和局部特征结构相结合的半自动解决方法，通过给定的特定体素点，结合边界模型找出特定组织的内部、外部体素，对边界体素计算汉森矩阵的特征值并按其特征结构进行分割．该方法只需要计算边界体素汉森矩阵的特征值，能够快速、有效地实现单一组织的分割．     

Multiview human pose estimation with unconstrained motions

We present a system for human pose estimation by using a single frame and without making assumptions on temporal coherence. The system uses 3D voxel data reconstructed from multiple synchronized video streams as input, and computes, for each frame, a skeleton model which best fits the body pose. This system adopts a hierarchical approach where the head and torso locations are found first based on template fitting with their specific shapes and dimensions. It is followed by a limb detection procedure that estimates the pose parameters of four limbs. However, a problem generally faced with skeleton models is the means to find adequate measurements to fit the model. In this paper, voxel data, together with two novel local shape features, are used for this purpose. Experiments show that this system is robust to several perturbations associated with the input data, such as voxel reconstruction errors and complex poses with self-contact, and also allows unconstrained motions, such as fast or unpredictable movements.     

A General Multi-step Algorithm for Voxel Traversing Along a Line

Traversing voxels along a three dimensional (3D) line is one of the most fundamental algorithms for voxel‐based applications. This paper presents a new 6‐connectivity integer algorithm for this task. The proposed algorithm accepts voxels having different sizes in x, y and z directions. To explain the idea of the proposed approach, a 2D algorithm is firstly considered and then extended in 3D. This algorithm is a multi‐step as up to three voxels may be added in one iteration. It accepts both integer and floating‐point input. The new algorithm was compared to other popular voxel traversing algorithms. Counting the number of arithmetic operations showed that the proposed algorithm requires the least amount of operations per traversed voxel. A comparison of spent CPU time using either integer or floating‐point arithmetic confirms that the proposed algorithm is the most efficient. This algorithm is simple, and in compact form which also makes it attractive for hardware implementation.     

Dual-branch residual network for lung nodule segmentation

An accurate segmentation of lung nodules in computed tomography (CT) images is critical to lung cancer analysis and diagnosis. However, due to the variety of lung nodules and the similarity of visual characteristics between nodules and their surroundings, a robust segmentation of nodules becomes a challenging problem. In this study, we propose the Dual-branch Residual Network (DB-ResNet) which is a data-driven model. Our approach integrates two new schemes to improve the generalization capability of the model: (1) the proposed model can simultaneously capture multi-view and multi-scale features of different nodules in CT images; (2) we combine the features of the intensity and the convolutional neural networks (CNN). We propose a pooling method, called the central intensity-pooling layer (CIP), to extract the intensity features of the center voxel of the block, and then use the CNN to obtain the convolutional features of the center voxel of the block. In addition, we designed a weighted sampling strategy based on the boundary of nodules for the selection of those voxels using the weighting score, to increase the accuracy of the model. The proposed method has been extensively evaluated on the LIDC-IDRI dataset containing 986 nodules. Experimental results show that the DB-ResNet achieves superior segmentation performance with the dice similarity coefficient (DSC) of 82.74% on the dataset. Moreover, we compared our results with those of four radiologists on the same dataset. The comparison showed that our DSC was 0.49% higher than that of human experts. This proves that our proposed method is as good as the experienced radiologist.     

A factorization-based approach for articulated nonrigid shape, motion and kinematic chain recovery from video

Recovering articulated shape and motion, especially human body motion, from video is a challenging problem with a wide range of applications in medical study, sport analysis and animation, etc. Previous work on articulated motion recovery generally requires prior knowledge of the kinematic chain and usually does not concern the recovery of the articulated shape. The non-rigidity of some articulated part, e.g. human body motion with nonrigid facial motion, is completely ignored. We propose a factorization-based approach to recover the shape, motion and kinematic chain of an articulated object with nonrigid parts altogether directly from video sequences under a unified framework. The proposed approach is based on our modeling of the articulated non-rigid motion as a set of intersecting motion subspaces. A motion subspace is the linear subspace of the trajectories of an object. It can model a rigid or non-rigid motion. The intersection of two motion subspaces of linked parts models the motion of an articulated joint or axis. Our approach consists of algorithms for motion segmentation, kinematic chain building, and shape recovery. It handles outliers and can be automated. We test our approach through synthetic and real experiments and demonstrate how to recover articulated structure with non-rigid parts via a single-view camera without prior knowledge of its kinematic chain.     

基于相关性的三维医学图像插值

现有的插值方法在进行医学断层图像插值时，要么不能兼顾灰度和形状的变化，要么计算量太大。为解决这一问题，文中提出一种基于相关性的三维医学图像插值算法。通过对原图进行门限分割，获得体素的分割值。对相同密度物质采用灰度插值，不同密度物质，利用体素的相关性来进行插值，使新的图像不仅在灰度上，而且在组织形状上，介于原来的断层图像之间，满足了医学图像插值的要求。与线性插值相比，新算法的视觉效果好，计算误差小；插值结果可有效地应用于构建三维体模型。     

Patch‐type Segmentation of Voxel Shapes using Simplified Surface Skeletons

We present a new method for decomposing a 3D voxel shape into disjoint segments using the shape's simplified surface‐skeleton. The surface skeleton of a shape consists of 2D manifolds inside its volume. Each skeleton point has a maximally inscribed ball that touches the boundary in at least two contact points. A key observation is that the boundaries of the simplified fore‐ and background skeletons map one‐to‐one to increasingly fuzzy, soft convex, respectively concave, edges of the shape. Using this property, we build a method for segmentation of 3D shapes which has several desirable properties. Our method segments both noisy shapes and shapes with soft edges which vanish over low‐curvature regions. Multiscale segmentations can be obtained by varying the simplification level of the skeleton. We present a voxel‐based implementation of our approach and illustrate it on several realistic examples.     

Brain tissue MR-image segmentation via optimum-path forest clustering

We present an accurate and fast approach for MR-image segmentation of brain tissues, that is robust to anatomical variations and takes an average of less than 1 min for completion on modern PCs. The method first corrects voxel values in the brain based on local estimations of the white-matter intensities. This strategy is inspired by other works, but it is simple, fast, and very effective. Tissue classification exploits a recent clustering approach based on the motion of optimum-path forest (OPF), which can find natural groups such that the absolute majority of voxels in each group belongs to the same class. First, a small random set of brain voxels is used for OPF clustering. Cluster labels are propagated to the remaining voxels, and then class labels are assigned to each group. The experiments used several datasets from three protocols (involving normal subjects, phantoms, and patients), two state-of-the-art approaches, and a novel methodology which finds the best choice of parameters for each method within the operational range of these parameters using a training dataset. The proposed method outperformed the compared approaches in speed, accuracy, and robustness.     

Probabilistic multiscale image segmentation

A method is presented to segment multidimensional images using a multiscale (hyperstack) approach with probabilistic linking. A hyperstack is a voxel-based multiscale data structure whose levels are constructed by convolving the original image with a Gaussian kernel of increasing width. Between voxels at adjacent scale levels, child-parent linkages are established according to a model-directed linkage scheme. In the resulting tree-like data structure, roots are formed to indicate the most plausible locations in scale space where segments in the original image are represented by a single voxel. The final segmentation is obtained by tracing back the linkages for all roots. The present paper deals with probabilistic (or multiparent) linking. The multiparent linkage structure is translated into a list of probabilities that are indicative of which voxels are partial volume voxels and to which extent. Probability maps are generated to visualize the progress of weak linkages in scale space when going from fine to coarser scale. It is demonstrated that probabilistic linking gives a significantly improved segmentation as compared with conventional (single-parent) linking     

2D Articulated Pose Tracking Using Particle Filter with Partitioned Sampling and Model Constraints

In this paper, we develop a two-dimensional articulated body tracking algorithm based on the particle filtering method using partitioned sampling and model constraints. Particle filtering has been proven to be an effective approach in the object tracking field, especially when dealing with single-object tracking. However, when applying it to human body tracking, we have to face a “particle-explosion” problem. We then introduce partitioned sampling, applied to a new articulated human body model, to solve this problem. Furthermore, we develop a propagating method originated from belief propagation (BP), which enables a set of particles to carry several constraints. The proposed algorithm is then applied to tracking articulated body motion in several testing scenarios. The experimental results indicate that the proposed algorithm is effective and reliable for 2D articulated pose tracking.     

A spatio-temporal 2D-models framework for human pose recovery in monocular sequences

This paper addresses the pose recovery problem of a particular articulated object: the human body. In this model-based approach, the 2D-shape is associated to the corresponding stick figure allowing the joint segmentation and pose recovery of the subject observed in the scene. The main disadvantage of 2D-models is their restriction to the viewpoint. To cope with this limitation, local spatio-temporal 2D-models corresponding to many views of the same sequences are trained, concatenated and sorted in a global framework. Temporal and spatial constraints are then considered to build the probabilistic transition matrix (PTM) that gives a frame to frame estimation of the most probable local models to use during the fitting procedure, thus limiting the feature space. This approach takes advantage of 3D information avoiding the use of a complex 3D human model. The experiments carried out on both indoor and outdoor sequences have demonstrated the ability of this approach to adequately segment pedestrians and estimate their poses independently of the direction of motion during the sequence.     

Displaying of details in subvoxel accuracy

Under the volume segmentation in voxel space,a lot of details,some fine and thin objects,are ignored.In order to accurately display these details,this paper has developed a methodology for volume segmentation in subvoxel space.In the subvoxel space,most of the “bridges”between adjacent layers are broken down.Based on the subvoxel space,an automatic segmentation algorithm reserving details is discussed.After segmentation,volume data in subvoxel space are reduced to original voxel space.Thus,the details with width of only one or several voxels are extracted and displayed.     

沿三维直线的非单位体素遍历的多步整数算法

提出一种只用整数运算的沿三维直线的体素遍历算法,适用的体素空间可以分割成非单位的和非正方体的.首先研究了二维平面中的体素直线遍历算法,然后提出一种以二维平面中的遍历算法为基础的沿三维直线的体素遍历算法.该算法是一个多步整数遍历算法,每一步可以遍历最多3个体素,且所用的判断公式非常精炼,不仅计算量很小而且没有累计误差.与现有的体素遍历算法进行比较的结果表明,该算法不仅没有累计误差,而且执行速度也是最快的.     

Decision trees for accelerating unimodal, hybrid and multimodal rendering models

This paper deals with the rendering of segmented unimodal, hybrid and aligned multimodal voxel models. We propose a data structure that classifies the segmented voxels into categories, so that whenever the model has to be traversed, only the selected categories are visited and the empty and non-selected voxels are skipped. This strategy is based on: (i) a decision tree, called the rendering decision tree (RDT), which represents the hierarchy of the classification process and (ii) an intermediate run-length encoding (RLE) of the classified voxel model. The traversal of the voxel model given a user query consists of two steps: first, the RDT is traversed and the set of selected categories computed; next, the RLE is visited, but the non-selected runs are skipped and only the voxels of the original model that are codified are accessed in selected runs of the RLE. This strategy has been used to render a voxel model by back-to-front traversal and splatting as well as to construct 3D textures for hardware-driven 3D texture mapping. The results show that the voxel model traversal is significantly accelerated.     

Geometry-shader-based real-time voxelization and applications

This work proposes a new voxelization algorithm based on newly available GPU functionalities and designs several real-time applications to render complex lighting effects with the voxelization result. The voxelization algorithm can efficiently transform a highly complex scene in a surface-boundary representation into a set of voxels in one GPU pass using the geometry shader. Newly available 3D textures are used to directly record the surficial and volumetric properties of objects such as opaqueness, refraction, and transmittance. In the first, the usage of 3D textures can remove those strenuous efforts required to modify the encoding and decoding scheme when adjusting the voxel resolution. Second, surficial and volumetric properties recorded in 3D textures can be used to interactively compute and render more realistic lighting effects including the shadow of objects with complex occlusion and the refraction and transmittance of transparent objects. The shadow can be rendered with an absorption coefficient which is computed according to the number of surfaces drawing in each voxel during voxelization and used to compute the amount of light passing through partially occluded complex objects. The surface normal, transmittance coefficient and refraction index recorded in each voxel can be used to simulate the refraction and transmittance lighting effects of transparent objects using our multiple-surfaced refraction algorithm. Finally, the results demonstrate that our algorithm can transform a dynamic scene into a set of voxels and render complex lighting effects in real time without any pre-processing.