Virtual reconstruction of archeological vessels using expert priors and intrinsic differential geometry information

This paper presents a method to assist in the tedious task of reconstructing ceramic vessels from unearthed archeological shards (fragments) using 3D computer vision-enabling technologies. The method exploits the shards surface intrinsic differential geometry information (one of many possible tools) coupled with a series of generic models to produce a virtual reconstruction and rendition of what the original vessel may have looked like. Generic models are constructed based on a host of factors including expert historical knowledge of the period, provenance of the artifact, and site location. The generic models need not to be identical to the original vessel, but they must be within a geometric transformation of it in most of its parts. The method is suited for ceramic vessels with some relief (i.e., surface with molding, carving, or stamping), as this method exploits surface intrinsic features for alignment. The alignment of the shards against the generic model uses a novel set of 3D weighted curve moments. The transformation is computed from corresponding parabolic contours on the shard and the generic model. A distance error metric is used to access the accuracy of alignment of a fragment to a given generic vessel model. The method is also extendable to surface markings. For a vessel that has no relief, color information or surface breaks can be used for the alignment. The method is tested on a subset of 3D scanned Independence National Historical Park (INDE) ceramic artifacts and the generic models created by experts. This work is part of an ongoing research activity in computational archeology that exploits many different tools and features to help in the mending process. Only the use of surface differential geometry information is reported here. This aspect is complementary to various other tools reported elsewhere by us and others, such as surface breaks, texture, color, etc.     

欠驱动水面机器人变周期全局渐近镇定控制

为了实现欠驱动水面机器人的全局渐近镇定,首先借助微分同胚变换,将欠驱动水面机器人的镇定问题转化成对由两个串级子系统构成的二阶欠驱动系统的镇定问题.针对转换后的系统,本文提出一种光滑的变周期控制方法,以实现对该系统的全局渐近镇定.与已有文献中控制律使用常数周期的方法相比,本文方法根据系统状态实时调整控制律周期,从而提高系统在原点附近的收敛速率.随后,基于上述方法,给出了水面机器人变周期控制算法,实现原系统的全局渐近镇定.最后,仿真对比结果验证所提变周期控制方法的有效性.     

基于全局运动对比度的轮廓编组元提取算法

在轮廓编组计算模型中,编组元的提取对于轮廓编组结果具有重要的影响。针对复杂场景中目标轮廓易与非目标边缘混淆的问题,提出了一种基于全局运动对比度的编组元提取算法。提出了基于边缘片段的运动相似度度量方法,并通过相似度定义了场景中的全局运动对比度,以此对非目标边缘片段进行抑制,从而提取出更为有效的目标轮廓边缘片段构成编组元集合。在Moseg_dataset数据集上的实验结果证明,提出的全局运动对比度对于非目标边缘片段具有良好的鉴别能力,相比较目前轮廓编组计算模型中基于边缘检测和轮廓检测的编组元提取算法,该算法显著降低了编组元集合的规模,提高了编组元集合的有效性。在相同的轮廓编组算法中,该算法提取的编组元集合能取得更优的编组结果。     

A knowledge-based system for the delineation of blood vessels on subtraction angiograms

A new approach to outline blood vessels on angiograms is described, based on a generic blood vessel model and four models for determining segment intersections. These models are generic in the sense that they only rely on the angiographic imaging technique and on the geometrical and topological properties of blood vessels. The models do not depend on specific anatomical knowledge. As a consequence, they can be used as an initial segmentation step to outline the coronary, cerebral as well as the carotid vessels. Results on clinical cerebral subtraction angiograms are presented.     

欠驱动船舶的光滑时变指数镇定

船舶水平面运动的欠驱动特性使船舶运动控制的研究具有了新的内涵．本文在船舶水平面运动的运动学及动力学模型基础上,将其演化为二阶系统方程组．针对其欠驱动的特性,借助微分同胚变换及控制输入变换将其转化为两个子系统,分别设计状态反馈控制律,从而得到了原系统的具有指数收敛速率的时变光滑反馈镇定律,实现闭环系统所有状态全局指数收敛至平衡点．该方法可用于欠驱动船舶动力定位或自动泊位控制．最后的仿真试验验证表明本方法是有效的．     

An automated pottery archival and reconstruction system

Motivated by the current requirements of archaeologists, we are developing an automated archival system for archaeological classification and reconstruction of ceramics. Our system uses the profile of an archaeological fragment, which is the cross‐section of the fragment in the direction of the rotational axis of symmetry, to classify and reconstruct it virtually. Ceramic fragments are recorded automatically by a 3D measurement system based on structured (coded) light. The input data for the estimation of the profile is a set of points produced by the acquisition system. By registering the front and the back views of the fragment the profile is computed and measurements like diameter, area percentage of the complete vessel, height and width are derived automatically. We demonstrate the method and give results on synthetic and real data. Copyright © 2003 John Wiley & Sons, Ltd.     

Tubular Structure Segmentation Based on Minimal Path Method and Anisotropic Enhancement

We present a new interactive method for tubular structure extraction. The main application and motivation for this work is vessel tracking in 2D and 3D images. The basic tools are minimal paths solved using the fast marching algorithm. This allows interactive tools for the physician by clicking on a small number of points in order to obtain a minimal path between two points or a set of paths in the case of a tree structure. Our method is based on a variant of the minimal path method that models the vessel as a centerline and surface. This is done by adding one dimension for the local radius around the centerline. The crucial step of our method is the definition of the local metrics to minimize. We have chosen to exploit the tubular structure of the vessels one wants to extract to built an anisotropic metric. The designed metric is well oriented along the direction of the vessel, admits higher velocity on the centerline, and provides a good estimate of the vessel radius. Based on the optimally oriented flux this measure is required to be robust against the disturbance introduced by noise or adjacent structures with intensity similar to the target vessel. We obtain promising results on noisy synthetic and real 2D and 3D images and we present a clinical validation.     

Retinal vessels segmentation based on level set and region growing

Retinal vessels play an important role in the diagnostic procedure of retinopathy. Accurate segmentation of retinal vessels is crucial for pathological analysis. In this paper, we propose a new retinal vessel segmentation method based on level set and region growing. Firstly, a retinal vessel image is preprocessed by the contrast-limited adaptive histogram equalization and a 2D Gabor wavelet to enhance the vessels. Then, an anisotropic diffusion filter is used to smooth the image and preserve vessel boundaries. Finally, the region growing method and a region-based active contour model with level set implementation are applied to extract retinal vessels, and their results are combined to achieve the final segmentation. Comparisons are conducted on the publicly available DRIVE and STARE databases using three different measurements. Experimental results show that the proposed method reaches an average accuracy of 94.77% on the DRIVE database and 95.09% on the STARE database.     

U-Net与自适应阈值脉冲耦合神经网络相结合的眼底血管分割方法

由于眼底血管结构复杂多变,且图像中血管与背景对比度低,眼底血管分割存在巨大困难,尤其是微小型血管难以分割.基于深层全卷积神经网络的U-Net能够有效提取血管图像全局及局部信息,但由于其输出为灰度图像,并采用硬阈值实现二值化,这会导致血管区域丢失、血管过细等问题.针对这些问题,提出一种结合U-Net与脉冲耦合神经网络(P...     

Combining volumetric dental CT and optical scan data for teeth modeling

Dental computer-aided design (CAD) systems have been intensively introduced to digital dentistry in recent years. As basic digital models, volumetric computed tomography (CT) images or optical surface scan data are used in most dental fields. In many fields, including orthodontics, complete teeth models are required for the diagnosis, planning and treatment purposes. In this research, we introduce a novel modeling approach combining dental CT images and an optically scanned surface to create complete individual teeth models. First, to classify crown and root regions for each set of data, corresponding pairs between two different data are determined based on their spatial relationship. The pairs are used to define the co-segmentation energy by introducing similarity and dissimilarity terms for each corresponding pair. Efficient global optimization can be performed by formulating a graph-cut problem to find the segmentation result that minimizes the energy. After classifying crown and root regions for each data set, complete individual teeth are obtained by merging the two different data sets. The advancing front method was successfully applied for merging purposes by considering the signed distance from the crown boundary of the surface mesh to the root surface of the CT. The teeth models which have detailed geometries obtained from the optically scanned surface and interstice regions recovered from volumetric data can be obtained using the proposed method. In addition, the suggested merging approach makes it possible to obtain complete teeth models from incomplete CT data with metal artifacts.     

A supervised approach for automated surface defect detection in ceramic tile quality control

Surface defect detection is very important to guarantee the quality of ceramic tiles production. At present, this process is usually performed manually in the ceramic tile industry, which is low efficiency and time-consuming. For small surface defects detection of high-resolution ceramic tiles image, an intelligent detection method for surface defects of ceramic tiles based on an improved you only look once version 5 (YOLOv5) algorithm is presented. Firstly, the high-resolution ceramic tile images are cropped into slices, and the Bottleneck module in the YOLOv5s network is optimized by introducing depthwise convolution and replaced in the whole network. Then, feature extraction is performed using the improved Shufflenetv2 backbone, and an attention mechanism is added to the backbone network to improve the feature extraction ability. The path aggregation network (PAN) and Feature Pyramid Networks (FPN) neck are used to enhance the feature extraction, and finally, the YOLO head is used to identify and locate the ceramic tile defects. The multiple sliding windows detection method is proposed to detect the original ceramic tile image which is faster than the single sliding window detection method. The experimental results show that compared with the original YOLOv5s detection algorithm, the parameters of the model are reduced by 20.46 %, the floating point operations are reduced by 26.22 %, and the mean average precision (mAP) of the proposed method is 96.73 % in the ceramic tile image slice test set which has 1.93 % improvement in mAP than the original YOLOv5s. Compare with other object detection methods, the method proposed in this paper also has certain advantages. In the high-resolution ceramic tile images test set, the mAP of the proposed algorithm is 86.44 % by using the multiple sliding window detection method. The ceramic defect detection experiment has verified the feasibility of the method proposed in this paper.     

Design of a maximally permissive liveness-enforcing supervisor with a compressed supervisory structure for flexible manufacturing systems

In this paper, a deadlock prevention policy for flexible manufacturing systems (FMS) is proposed, which can obtain a maximally permissive liveness-enforcing Petri net supervisor while the number of control places is compressed. By using a vector covering approach, the sets of legal markings and first-met bad markings (FBM) are reduced to two small ones, i.e., the minimal covering set of legal markings and the minimal covered set of FBM. A maximally permissive control purpose can be achieved by designing control places such that all markings in the minimal covered set of FBM are forbidden and no marking in the minimal covering set of legal markings is forbidden. An integer linear programming problem is designed to minimize the number of control places under an assumption that a control place is associated with a P-semiflow. The resulting net has the minimal number of control places on the premise that the assumption holds, and possesses all permissive states of a plant. The only problem of the proposed method is its computational complexity that makes it inapplicable to large-scale Petri net models. An FMS example from the literature is presented to illustrate the proposed method.     

Automatic Surface Reconstruction from Point Sets in Space

In this paper an algorithm is proposed that takes as input a generic set of unorganized points, sampled on a real object, and returns a closed interpolating surface. Specifically, this method generates a closed 2-manifold surface made of triangular faces, without limitations on the shape or genus of the original solid. The reconstruction method is based on generation of the Delaunay tetrahedralization of the point set, followed by a sculpturing process constrained to particular criteria. The main applications of this tool are in medical analysis and in reverse engineering areas. It is possible, for example, to reconstruct anatomical parts starting from surveys based on TACs or magnetic resonance.     

Effective visualization of complex vascular structures using a non-parametric vessel detection method

The effective visualization of vascular structures is critical for diagnosis, surgical planning as well as treatment evaluation. In recent work, we have developed an algorithm for vessel detection that examines the intensity profile around each voxel in an angiographic image and determines the likelihood that any given voxel belongs to a vessel; we term this the "vesselness coefficient" of the voxel. Our results show that our algorithm works particularly well for visualizing branch points in vessels. Compared to standard Hessian based techniques, which are fine-tuned to identify long cylindrical structures, our technique identifies branches and connections with other vessels. Using our computed vesselness coefficient, we explore a set of techniques for visualizing vasculature. Visualizing vessels is particularly challenging because not only is their position in space important for clinicians but it is also important to be able to resolve their spatial relationship. We applied visualization techniques that provide shape cues as well as depth cues to allow the viewer to differentiate between vessels that are closer from those that are farther. We use our computed vesselness coefficient to effectively visualize vasculature in both clinical neurovascular x-ray computed tomography based angiography images, as well as images from three different animal studies. We conducted a formal user evaluation of our visualization techniques with the help of radiologists, surgeons, and other expert users. Results indicate that experts preferred distance color blending and tone shading for conveying depth over standard visualization techniques.     

基于群体智能的三维碎片全局最优匹配方法

针对传统三维碎片整体匹配过程中误差积累的问题,提出了一种基于群体智能的全局最优匹配方法。该方法对破碎物体的三维多碎片全局匹配建立全局整体碎片匹配的数学模型,将碎片的整体最优匹配求解问题转换为求满足一定约束条件的最优匹配矩阵的组合优化问题,通过将自然社会认知优化算法进行离散化来求解该NP问题。典型实例分析验证了所提方法全局优化能力强,与初始位置无关,有较强的鲁棒性,为三维碎片整体匹配提供一个有效的方法。     

三维图像多相分割的变分水平集方法

变分水平集方法是图像分割等领域出现的新的建模方法,借助多个水平集函数可有效地实现图像多相分割.但在区域/相的通用表达、不同区域内图像模型的表达、通用的能量函的设计、高维图像分割中的拓展研究等方面仍是图像处理的变分方法、水平集方法、偏微分方程方法等研究的热点问题.文中以三维图像为研究对象,系统地建立了一种新的三维图像多相分割的变分水平集方法.该方法用n-1个水平集函数划分n个区域,并基于Heaviside函数设汁出区域划分的通用的特征函数;其能量泛函包括通用的区域模型、边缘检测模型和水平集函数为符号距离函数的约束项3部分;最后,针对所得到的曲面演化方程,采用半隐式差分格式进行离散,并对多种类型三维图像进行分割验证了所提出模型的通用性和有效性.     

一种视网膜血管自适应提取方法

为了快速有效地提取视网膜血管，根据视网膜图像的灰度分布特征，提出了一种新的基于自适应阈值化的血管提取方法。该方法是首先把图像划分成很多同样尺寸的小子图像，然后在每个子图像中分别计算局部阈值，并用该阈值分割该子图像。因为视网膜图像中血管和背景在局部范围内都比较均匀，所以在每个子图像中都存在一个局部阈值能够将其中的血管分割出来。采用的局部阈值计算方法不仅允许子图像可以取得很小，而且能够保证得到平方误差最小意义下的最优阈值。在阈值计算过程中，还用到一种基于过零点边缘检测技术的边缘追踪算法。最后还提出一种基于区域生长的特征综合方法，即通过综合两次阈值化分割得到的血管结构来清除碎片。多幅视网膜图像的实验证明，该方法的计算速度很快，并且可以提取包括细血管在内的绝大部分血管。     

基于区域增长与局部自适应C-V模型的脑血管分割

提出了一种针对TOF MRA(time-of-flight magnetic resonance angiography)磁共振图像的双重分割脑血管提取方法。首先结合高斯滤波,采用二维OTSU算法,结合MIP(maximum intensity projection)图像获得三维血管种子点,定义全局与局部信息相结合的区域增长规则,通过区域增长算法对血管进行粗分割;然后,采用 Catt 扩散模型对体数据场进行各向异性滤波,提出了局部自适应C-V模型,将初步分割结果作为自适应活动轮廓模型的初始轮廓线进行二次分割。实验结果表明,该算法不仅能够有效分割脑血管粗大分支,而且还能精确提取脑血管的细小结构。     

灰度不均的弱边缘血管影像的水平集分割方法

针对血管影像中灰度不均和弱边缘情况下已有水平集模型不能正确分割血管问题,提出一种耦合了血管影像的几何信息、边缘信息和区域信息的水平集分割方法.首先,采用Hessian矩阵的各向异性性对血管状目标进行识别,对原始影像数据进行多尺度滤波;然后采用拉普拉斯算子零交叉点的快速边缘积分方法将边缘信息嵌入能量泛函中,构建一种基于结构、边缘和区域信息的水平集分割方法.相比于单一依靠影像边缘信息或区域信息模型及其改进模型,该方法在分割严重灰度不均匀的血管造影影像上能够准确提取血管,并精确定位血管边缘.