Recovery of 3D animal motions using cameras and mirrors

We present a system that tracks an articulated body performing 3D movement with occlusions using a combination of cameras and mirrors. By integrating cameras and mirrors we get a simultaneous coverage of almost every point on the target and avoid occlusions. The suggested setup is much simpler and easier to handle compared to the equivalent, camera-based setup. Our tracking algorithm is model-based, and errors in the model are treated using the bundle adjustment procedure. In order to deal with the problem of feature visibility, each feature is set to be valid or invalid based on the model and on its expected appearance; this ensures that the system always tracks a set of distinguishable features. The proposed algorithm was able to track targets in 3D using the Gauss–Newton method to minimize geometric errors. We tested our setup by tracking the chameleon’s eyes. Tracking the eyes of a chameleon can be considered as the estimation of the 3D pose of an articulated body, where the head of the chameleon is considered as a rigid body, and each of the two eyes has additional two degrees of freedom. The algorithm proposed can be easily expanded to cope with more complex objects.     

Editing tools for 3D medical imaging

A set of tools called MPR Edit (multiplanar reconstruction editor) is presented. Designed like a paint and drawing program for 3-D data sets, MPR Edit allows the interactive creation of shapes that define volumes of interest in images of medical data. The implementation of MPR Edit is described, and examples of its use are given. Clinical applications are discussed     

低端嵌入式设备3D图形库的设计

针对低端嵌入式设备运算速度较低,存储空间较小且不支持通用3D图形库的局限性,通过分析常规3D渲染管线和算法,对次要功能实施裁减,设计出一个简化的3D图形库.经在低性能手机上测试,此方案渲染效果良好,切实可行.     

Accelerating 3D medical volume segmentation using GPUs

Multimedia Tools and Applications - Medical images have an undeniably integral role in the process of diagnosing and treating of a very large number of ailments. Processing such images (for...     

沉浸式立体显示技术在临床医学领域中的应用

随着现代科学技术的快速发展,立体显示技术为临床医生的双眼视觉功能和临床应用场景,提供了现实模拟度更高的载体,并成为当前计算机视觉和临床医学领域共同研究的热点。在微创手术术前,与传统的平面显示技术相比,沉浸式立体显示技术能够提供更生动、准确的3维人体生理和病理影像,使医生更易于判断病变的层次、形状和血管等复杂结构及解剖关系;同时虚拟现实能够为医学培训及手术预演提供沉浸式的手术情境模拟,帮助医生高效地掌握手术技巧,提高医学术前诊断效率,从而进一步降低手术风险。在微创手术术中,基于增强现实的三维成像导航技术,能够将微创手术过程立体、直观地展现在医生面前,使术区各组织及其与手术器械间的位置关系和距离更加容易判断,同时通过叠加相同区域的术前检查影像,为手术提供实时的路径导航,实现精准微创手术。此外,在临床医疗资源共享中占据重要比重的远程诊疗领域,立体显示技术能够为远程诊断、线上会诊以及机器人手术等提供更为精确的深度信息,以及更多维度的图像信息,使医学数据的远程显示结果更具有真实性和实用性。现阶段立体显示技术在临床医学领域中也存在显示模式转换不舒适、三维重建图像信息缺失以及立体显示软、硬件系统带来的视觉疲劳等问题,但该技术在医学领域已经展露头角,在未来的临床医学进步中会成为不可或缺的一部分。本文详细分析了沉浸式立体显示技术在临床医学中的代表性应用,介绍了微创外科手术以及远程诊疗领域国内外的研究现状,从影像诊断、手术训练、规划与导航、治疗和教育培训4个方面,总结了立体显示技术在临床医学领域中的研究进展。     

三维图像边缘曲面模型交互式选择抽取算法

提出一种新的三维图像边缘曲面模型选择抽取算法,能以交互方式获取用户感兴趣的三维边缘曲面模型.用户在二维断层图像的切片区选择目标三维边缘曲面模型对应的二维区域,将选定坐标映射到三维区域,利用基于Laplacian算子的三维边缘检测算子检测三维区域内的部分边缘立方体,除噪后置为种子立方体,根据立方体共面和三维区域增长法原理追踪获得用户感兴趣的具有亚体素精度三维图像边缘曲面模型.     

3D multimodality medical image registration using morphological tools

Multimodal medical images are often of too different a nature to be registered on the basis of the image grey values only. It is the purpose of this paper to construct operators that extract similar structures from these images that will enable rigid registration by simple grey value based methods, such as maximization of cross-correlation. These operators can be constructed using only basic morphological tools such as erosion and dilation. Simple versions of these operators are easily implemented on any computer system. We will show that accurate registration of images of various modalities (MR, CT, SPECT and PET) can be obtained using this approach.     

针对医学重构模型的3D分割及压缩算法

提供便捷的交互手段,从3D重构模型中精确分割感兴趣区域,为医生术前手术规划提供更为直观和准确的诊断信息。利用VTK（Visualization ToolKit）开发包构架系统平台,采用3D区域生长法分割感兴趣部位,结合数学形态法消除数据的病态关联,实现精确分割,进一步修改数据掩模完成分割后数据的压缩存储。实验证明了上述算法的有效性,可得到良好的3D分割效果。     

Template-based 3D model fitting using dual-domain relaxation

We introduce a template fitting method for 3D surface meshes. A given template mesh is deformed to closely approximate the input 3D geometry. The connectivity of the deformed template model is automatically adjusted to facilitate the geometric fitting and to ascertain high quality of the mesh elements. The template fitting process utilizes a specially tailored Laplacian processing framework, where in the first, coarse fitting stage we approximate the input geometry with a linearized biharmonic surface (a variant of LS-mesh), and then the fine geometric detail is fitted further using iterative Laplacian editing with reliable correspondence constraints and a local surface flattening mechanism to avoid foldovers. The latter step is performed in the dual mesh domain, which is shown to encourage near-equilateral mesh elements and significantly reduces the occurrence of triangle foldovers, a well-known problem in mesh fitting. To experimentally evaluate our approach, we compare our method with relevant state-of-the-art techniques and confirm significant improvements of results. In addition, we demonstrate the usefulness of our approach to the application of consistent surface parameterization (also known as cross-parameterization).     

改进混合策略的多分辨率3D医学图像配准

基于混合策略的多分辨率算法是当前3D医学图像刚体配准中普遍采用的方法,不过其仅仅是优化算法的混合。通过研究不同分辨率对一阶互信息（常称为互信息）和二阶互信息配准的影响,在二级多分辨率策略的配准中,各级采用相对更适合的相似性测度,提出了混合优化算法和混合测度的改进算法。实验表明,改进算法在配准精度上达到了亚体素级,且明显优于基于单一测度的算法,在配准速度上远远快于基于二阶互信息单一测度的算法,略慢于基于一阶互信息单一测度的算法。     

Multi-orientation geometric medical volumes segmentation using 3D multiresolution analysis

Multimedia Tools and Applications - Medical images have a very significant impact in the diagnosing and treating process of patient ailments and radiology applications. For many reasons, processing...     

Improved 3D reconstruction in smart-room environments using ToF imaging

This paper presents the use of Time-of-Flight (ToF) cameras in smart-rooms and how this leads to improved results in segmenting the people in the room from the background and consequently better 3D reconstruction of foreground objects. A calibrated rig consisting of one Swissranger SR3100 Time-of-Flight range camera and a high resolution standard CCD camera is set in a smart-room containing five other standard cameras. A probabilistic background model is used to segment each view and a shape from silhouette volume is reconstructed. It is shown that the presence of the range camera gives ways of eliminating regional artifacts and therefore creating a more robust input for higher level applications such as people tracking or human motion analysis.     

D3FC: deep feature-extractor discriminative dictionary-learning fuzzy classifier for medical imaging

Applied Intelligence - Providing accurate and speedy diagnosis and, in turn, treatment, automated medical image analysis plays a significant role in survival rate improvement. Inherent different...     

Pose-invariant face recognition using a 3D deformable model

The paper proposes a novel, pose-invariant face recognition system based on a deformable, generic 3D face model, that is a composite of: (1) an edge model, (2) a color region model and (3) a wireframe model for jointly describing the shape and important features of the face. The first two submodels are used for image analysis and the third mainly for face synthesis. In order to match the model to face images in arbitrary poses, the 3D model can be projected onto different 2D viewplanes based on rotation, translation and scale parameters, thereby generating multiple face-image templates (in different sizes and orientations). Face shape variations among people are taken into account by the deformation parameters of the model. Given an unknown face, its pose is estimated by model matching and the system synthesizes face images of known subjects in the same pose. The face is then classified as the subject whose synthesized image is most similar. The synthesized images are generated using a 3D face representation scheme which encodes the 3D shape and texture characteristics of the faces. This face representation is automatically derived from training face images of the subject. Experimental results show that the method is capable of determining pose and recognizing faces accurately over a wide range of poses and with naturally varying lighting conditions. Recognition rates of 92.3% have been achieved by the method with 10 training face images per person.