基于ICP和CPD的颅骨自动配准算法

颅骨配准是计算机辅助的三维颅面复原技术的重要研究内容之一.颅骨配准的准确与否会直接影响到将来颅面复原的准确性.为此,提出一种新的3D颅骨自动配准算法.该算法考虑到颅骨模型的特殊结构与实现的简便性,首先自动提取颅骨不光滑区域的脊线(Crest lines)以及光滑区域的顶点作为特征点,然后利用迭代最近点(ICP)算法进行粗配准,在此基础上,再采用CPD(Coherent Point Drift)算法对颅骨进行精确配准.实验结果表明,该算法能有效提高颅骨配准的准确性并对缺损颅骨具有一定的鲁棒性.     

颅骨点云模型的局部特征配准方法

目的 点云配准是计算机视觉领域里的一个研究热点,其应用领域涉及3维重建、目标识别、颅面复原等多个方面。颅骨配准是颅面复原的一个重要步骤,其配准的正确与否将直接影响到颅面复原的结果。为了提高颅骨配准的精度和收敛速度,提出一种基于局部特征的颅骨点云模型配准方法。方法 首先提取颅骨点云模型的局部深度、法线偏角和点云密度等局部特征;然后计算局部特征点集的相关性,得到相关候选点集,并通过删减外点实现颅骨点云的粗配准;最后采用基于高斯概率模型和动态迭代系数的改进迭代最近点 （ICP） 算法实现颅骨的细配准。结果 通过对公共点云数据模型以及颅骨点云数据模型分别进行配准实验,结果表明,基于局部特征的点云配准算法可以完成点云模型的精确配准,特别是对颅骨点云模型具有较好的配准效果。在颅骨细配准阶段,跟ICP算法相比,改进ICP算法的配准精度和收敛速度分别提高了约30%和60%;跟概率迭代最近点 （PCP） 算法相比,其配准精度差异不大,收敛速度提高了约50%。结论 基于局部特征的点云配准算法不仅可以用于公共点云数据模型的精确配准,而且更适用于颅骨点云数据模型的配准,是一种精度高、速度快的颅骨点云模型配准方法。     

基于颅骨自动配准的颅面复原方法

主要介绍了基于颅骨配准的颅面复原方法,其中颅骨配准算法采用ICP+CPD方法。试验表明,该方法复原效果良好,并对缺损颅骨具有一定的鲁棒性。     

特征点自动标定的颅面复原及其评估方法

为了采用计算机技术从人头颅骨样本重构出个性化的三维表面面部特征,提出一种特征点自动标定的颅面复原算法.首先利用特征点自动标定算法对待复原颅骨模型进行特征点标定;然后在基于模板变形的颅面复原算法基础上引入Laplace坐标网格变形技术,以实现待复原颅骨模型的颅面复原.为了验证文中算法进行颅面复原结果的可靠性,提出一种采用计算颅面整体特征和局部特征相似度的颅面复原结果评估方法.实验结果表明,文中提出的特征点自动标定算法大大提高了颅面复原前期工作的效率,颅面复原算法对待复原颅骨的颅面复原取得了良好的复原效果;同时,颅面复原结果评估方法达到了主观与客观评价上的一致,对复原结果验证有良好的指导作用.     

数据配准的颅骨面貌复原方法

颅骨面貌复原技术在法医刑侦领域具有重要应用,为了估计给定颅骨的未知面貌,提出点云配准与五官(鼻、眼睛、嘴)配准结合的颅骨面貌复原框架及基于软组织厚度的评价方法.首先构建颅面数据库并进行坐标校正;然后确定配准点集及其对应关系,采用ICP算法和薄板样条函数两次配准实现参考颅骨与待复原颅骨的精确配准,并将参考模型的软组织厚度...     

加入迭代因子的层次化颅骨配准方法

目的 在基于知识的颅面复原中,为了对未知颅骨的面貌进行复原,需要在颅骨库里寻找相似颅骨,将相似颅骨的面皮作为参考。寻找相似颅骨的过程即颅骨配准,配准的精度和效率是两个重要性能指标。本文提出一种基于特征区域和改进ICP（iterative closest point）算法的层次化颅骨配准方法。方法 首先,将颅骨模型去噪、简化并归一化,通过计算体积积分不变量,确定每个点的凹凸性;使用K-means方法,将颅骨上的点聚类为多个或凹或凸的特征区域。然后,通过主成分分析法来计算两个颅骨的相似特征区域,对每一个可能的匹配计算3维变换,将两个颅骨粗略对齐;最后,采用加入迭代因子的方法对ICP算法进行改进,使用改进的ICP算法对颅骨进行精配准。结果 将本文方法用于颅骨模型、兵马俑模型以及公共数据集中的3维模型配准,经典ICP算法的配准时间分别为6.23 s、7.61 s、4.17 s,改进的ICP算法配准时间分别为3.02 s、3.23 s、2.83 s,算法效率提高了约2倍,配准效果也有明显提高。实验中通过对迭代因子的测试,发现不同的数据集需要设定不同的迭代因子。结论 本文所提出的基于区域特征的层次化配准方法提高了颅骨配准的精度和效率,整个过程不需要人工干预,该算法具有一定的普适性,可用于相似3维模型配准。     

基于边缘对应的三维颅骨自动非刚性配准方法

针对三维颅骨模型在初始姿态相差较大以及存在较多缺失情况下自动配准困难的问题,提出一种基于边缘对应的三维颅骨非刚性自动配准方法。首先对待配准三维颅骨进行边缘提取,获得所有孔洞的边缘;然后根据边缘长度以及边缘间最短距离自动识别边缘类型,建立待配准颅骨和参考颅骨在边缘上的对应;之后对待配准颅骨的初始位置和姿态进行调整,实现粗配准;最后通过两次一致点漂移（CPD）算法逐步实现两个颅骨从边缘区域至所有区域的精确配准。实验结果表明,与常用的基于迭代最近点（ICP）和薄板样条函数（TPS）相结合的三维颅骨自动配准方法相比,该方法对姿态、位置、分辨率以及缺损具有更强的鲁棒性,并且配准效率更高。     

三维颅骨表面模型的复杂孔洞修补

颅骨表面模型的孔洞复杂,很难用目前常用的图形学中某一类算法进行修补。目前基本修补算法（BHRA）可用来修补一般区域孔洞,但对于颅骨上破损较大复杂孔洞和特征区域复杂孔洞还没有较好的修补算法,为此提出了一种颅骨模型复杂孔洞修补算法,该算法首先通过复杂孔洞的位置和复杂孔洞包围盒的面积来对该复杂孔洞进行分类,再选择相应的算法进行孔洞修补。针对颅骨上区域较大复杂孔洞,提出了一种向内递归修补法(IRS),解决了传统孔洞修补方法修补曲面较为平坦的问题;针对颅骨上的特征区域复杂孔洞,提出了特征模型匹配法（TMA）,使用标准模型作为约束并对其进行变形,使修补后的模型更符合人的面部特征。实验结果分析表明,本算法对颅骨上区域较大的复杂孔洞和特征区域孔洞的修补效果令人满意,同时将该修补后的颅骨模型进行颅面复原,颅面复原效果良好。     

图像配准技术在颅像重合中的应用

该文将图像配准技术应用于颅像重合,从而实现颅骨与照片的配准。首先简要介绍了常用的几种图像配准算法,然后根据颅像配准的特点选择基于特征点映射的配准算法,并结合加权值的思想使颅像配准效果更加理想,最后对颅像配准的误差来源进行了分析。试验证明,采用基于加权值的特征点映射配准算法获得了较好的配准效果。     

三维颅骨模型获取方法研究

颅面复原是一种对人类的颅骨进行面部容貌复原的技术,该技术以人类学、法医学、解剖学中的头骨与面貌相互关系规律为科学依据,广泛应用于考古、刑侦等领域。其中获取三维颅骨模型是实现复原的第一步,颅骨模型的准确与否会直接影响到将来颅面复原的准确性。文中主要介绍了两种获取三维颅骨模型的方法,一种是对cT图像进行面绘制三维重构,另一种是使用三维扫描仪获取三维颅骨模型。之后对获取的颅骨模型建立法兰克福坐标系。实验表明,通过这两种方法获取的颅骨模型为颅面复原提供了良好的颅骨原始数据。     

The Craniofacial Reconstruction from the Local Structural Diversity of Skulls

The craniofacial reconstruction is employed as an initialization of the identification from skulls in forensics. In this paper, we present a two‐level craniofacial reconstruction framework based on the local structural diversity of the skulls. On the low level, the holistic reconstruction is formulated as the superimposition of the selected tissue map on the novel skull. The crux is the accurate map registration, which is implemented as a warping guided by the 2D feature curve patterns. The curve pattern extraction under an energy minimization framework is proposed for the automatic feature labeling on the skull depth map. The feature configuration on the warped tissue map is expected to resemble that on the novel skull. In order to make the reconstructed faces personalized, on the high level, the local facial features are estimated from the skull measurements via a RBF model. The RBF model is learnt from a dataset of the skull and the face feature pairs extracted from the head volume data. The experiments demonstrate the facial outlooks can be reconstructed feasibly and efficiently.     

基于多尺度投影的相似颅骨检索

在基于模板变形的颅面复原方法中,复原的开始阶段需要在数据库中选取与待复原颅骨最为相似的参考颅骨.鉴于基于三维模型的检索算法时间久且颅骨间的差异细微,从而不同于一般三维模型数据库中各模型的差异.因此,已有的三维模型检索算法不适用于颅骨检索.本文提出一种夹角信息和距离信息融合的颅骨轮廓特征提取算法,并在此基础上提出一种能够反映颅骨空域信息的剖面特征提取算法.检索时首先获取三维颅骨的正交投影和深度投影,通过正交投影获取轮廓的角度和距离特征,通过深度投影获得具有空域信息的剖面特征;然后对多个特征进行加权融合搜索到最相似颅骨;最后通过ICP+TPS对检索到的颅骨进行误差评估.实验表明,本算法在保证检索效率的同时,可以准确地应用在颅面复原前期对最相似颅骨的选择上.     

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

3D 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.     

改进的基于特征点软组织厚度的颅面复原方法

现有三维颅面复原技术大多依据颅骨特征点的软组织厚度统计值。针对现有统计值指标涵盖的年龄、胖瘦等属性段较宽泛导致复原面貌缺乏个性的缺点,提出了一种改进方法。首先通过CT扫描仪获得颅面样本数据,并通过图像重构获得三维颅骨和人脸模型;然后采用一种半自动特征点标定方法对三维颅骨样本进行特征点标定,并求解特征点软组织厚度;之后采用支持向量回归方法构建特征点软组织厚度与属性之间的函数关系;最后根据待复原颅骨的属性以及回归函数计算特征点软组织厚度,在此基础上采用薄板样条函数对参考人脸模型进行变形获得复原面貌。实验结果表明,相比于已有方法,该方法能获得更准确的软组织厚度,提高颅面复原的准确度。     

Performance evaluation of memetic approaches in 3D reconstruction of forensic objects

Different tasks in forensics require the use of 3D models of forensic objects (skulls, bones, corpses, etc.) captured by 3D range scanners. Since a whole object cannot be completely scanned in a single image using a range scanner, multiple acquisitions from different views are needed to supply the information to construct the 3D model by a range image registration method. There is an increasing interest in adopting evolutionary algorithms as the optimization technique for image registration methods. However, the image registration community tends to separate global and local searches in two different stages, named sequential hybridization approach, which is opposite to the scheme adopted by the memetic framework. In this work, we aim to analyze the capabilities of memetic algorithms (Moscato in On evolution, search, optimization, genetic algorithms and martial arts: towards memeticalgorithms. Report 826, Caltech Concurrent Computation Program, Pasadena, 1989) for tackling a really complex and challenging real-world problem as the 3D reconstruction of forensic objects. Our intention is threefold: firstly, designing new memetic-based methods for tackling a real-world problem and subsequently carrying out a performance and behavioral analysis of the results; secondly, comparing their performance with the one achieved by other methods based on the classical sequential hybridization approach; and thirdly, concluding the experimental study by highlighting the outcomes achieved by the best method in tackling the real-world problem. Several real-world 3D reconstruction problems from the Physical Anthropology Lab at the University of Granada, Spain, were used to support the evaluation study.     

A scatter search-based technique for pair-wise 3D range image registration in forensic anthropology

Different tasks in forensic anthropology require the use of three-dimensional models of forensic objects (skulls, bones, corpses, etc) captured by 3D range scanners. Since a whole object cannot be completely scanned with a single image, multiple scans from different views are needed to supply the information to construct the 3D model. Range image registration methods study the accurate integration of the different views acquired by range scanners, with pair-wise approaches progressively processing every adjacent pair of scanned views until reconstructing the whole 3D model of the object. Our proposal is based on the adaptation of our previous work (Cordon et al, IEEE Conference on Evolutionary Computation, pp 2738–2744, 2005 in Pattern Recognit Lett 27(11); 1191-1200, 2006) in order to apply the scatter search evolutionary algorithm to pair-wise image registration in forensic anthropology applications. To measure the performance of this adaptation, we design an experimental setup considering some of the most recent and accurate evolutionary techniques for the problem, as well as one skull from our Physical Anthropology Lab. Two additional volumes, commonly used in other pair-wise range IR contributions, have also been considered to complement the comparison of results among the proposals. This work was supported by the Ministerio de Ciencia y Tecnología of Spain under project TIC2003-00877 (including FEDER fundings).     

基于凸包算法的三维表面重建中边缘轮廓提取

在三维表面重建过程中，边缘轮廓的提取起着关键的作用。为了对头部CT图片中的颅骨边缘进行有效地提取，提出利用平面凸包进行边缘轮廓提取的方法。利用该方法提取出头部CT图像中的颅骨边缘，并将其作为三维表面重建中的轮廓输人，取得了良好的效果。同时，将通过该方法得到的颅骨边缘应用于基于颅骨的三维面貌复原技术研究中，为面貌复原技术的发展提供了新的思路。