Silhouette内固定系统治疗脊椎骨折的疗效分析

目的探讨Silhouette内固定系统联合椎体内注入医用硫酸钙与椎弓根螺钉内固定术治疗脊椎骨折的临床疗效。方法选择胸腰椎骨折患者80例,随机分为观察者和对照组,各40例,对照组患者行传统切开椎弓根螺钉内固定术治疗,观察者患者行Silhouette内固定系统联合椎体内注入医用硫酸钙,术后常规治疗,并定期随访,比较两组患者围术期参数、Franke1分级、临床指标、临床疗效以及影像学差异。结果观察者患者手术时间、手术出血量、住院时间等围术期参数以及VAS评分均低于对照组(P<0.05);治疗后两组患者的Franke1分级均较治疗前优,且观察组明显优于对照组(P<0.05);治疗后两组患者椎管侵占率和Cobb角均有明显降低,伤椎前高压缩比明显升高(P<0.05);观察组患者NEER评分优良率明显高于对照组(P<0.05);术后观察组患者CSC吸收良好,椎体高度未见有变化,影像学表现相对优于对照组。结论与传统的椎弓根螺钉内固定术相比,椎体内注入医用硫酸钙联合Silhouette内固定系统治疗的患者影像学表现和临床疗效较好,值得临床推广使用。

Topology evolutions of silhouettes

We give the topology changing of the silhouette in 3D space while others study the projections in an image. Silhou- ettes play a crucial role in visualization, graphics and vision. This work focuses on the global behaviors of silhouettes, especially their topological evolutions, such as splitting, merging, appearing and disappearing. The dynamics of silhouettes are governed by the topology, the curvature of the surface, and the view point. In this paper, we work on a more theoretical level to give enu- merative properties of the silhouette including: the integration of signed geodesic curvature along a silhouette is equal to the view cone angle; in elliptic regions, no silhouette can be contained in another one; in hyperbolic regions, if a silhouette is homotopic to a point, then it has at least 4 cusps; finally, critical events can only happen when the view point is on the aspect surfaces (ruled surface of the asymptotic lines of parabolic points with surface itself). We also introduce a method to visualize the evolution of silhouettes, especially all the critical events where the topologies of the silhouettes change. The results have broad applications in computer vision for recognition, graphics for rendering and visualization.

Panicle-3D: A low-cost 3D-modeling method for rice panicles based on deep learning, shape from silhouette, and supervoxel clustering

Self-occlusions are common in rice canopy images and strongly influence the calculation accuracies of panicle traits. Such interference can be largely eliminated if panicles are phenotyped at the 3 D level.Research on 3 D panicle phenotyping has been limited. Given that existing 3 D modeling techniques do not focus on specified parts of a target object, an efficient method for panicle modeling of large numbers of rice plants is lacking. This paper presents an automatic and nondestructive method for 3 D panicle modeling. The proposed method integrates shoot rice reconstruction with shape from silhouette, 2 D panicle segmentation with a deep convolutional neural network, and 3 D panicle segmentation with ray tracing and supervoxel clustering. A multiview imaging system was built to acquire image sequences of rice canopies with an efficiency of approximately 4 min per rice plant. The execution time of panicle modeling per rice plant using 90 images was approximately 26 min. The outputs of the algorithm for a single rice plant are a shoot rice model, surface shoot rice model, panicle model, and surface panicle model, all represented by a list of spatial coordinates. The efficiency and performance were evaluated and compared with the classical structure-from-motion algorithm. The results demonstrated that the proposed method is well qualified to recover the 3 D shapes of rice panicles from multiview images and is readily adaptable to rice plants of diverse accessions and growth stages. The proposed algorithm is superior to the structure-from-motion method in terms of texture preservation and computational efficiency. The sample images and implementation of the algorithm are available online. This automatic, cost-efficient, and nondestructive method of 3 D panicle modeling may be applied to high-throughput 3 D phenotyping of large rice populations.

Investigation of Jacket Silhouette Based on Ease Distribution

The silhouette of women's garment is diversified in its design. In this paper,a new model was developed to predict the silhouette of jacket for the given girth measurements. Sixty-three princess-seam women's jackets with seven styles and nine girth dimensions of each style were produced. Their ease distribution was investigated at bustline,waistline,and hipline,respectively. The widths of the cross-sections for bustline,waistline,and hipline were formulated by multiple linear regressions. Then the silhouette was captured from the ratio of waist width to bust width of a designed jacket,together with that of waist width to hip width.

IDENTIFICATION OF 3-D OBJECTS FROM THREE ORTHOGONAL SILHOUETTES USING NORMALIZED LINEAR QUAD-OCTREES

Linear octrees offer a volume representation of 3-D objects, which is quite compactand lends itself to traditional object processing operations. However, the linear octree structurefor generating the representation of 3-D objects from three orthogonal silhouettes by using thevolume intersection technique is dependent on viewpoints. The recognition achieved from match-ing object representations to model representations requires that the representations of objectsare independent of viewpoints. In order to obtain independent representations of viewpoints,the three principal axes of the object should be obtained from the moment of inertia matrix bycomputing its eigenvectors. The linear octree is projected onto the image planes of the three prin-cipal views (along the principal axes) to obtain the three normalized linear quadtrees. The objectmatching procedure has two phases: the first phase is to match the normalized linear quadtrees ofthe unknown object to a subset of models contained in a library utilizing a measure of symmetricdifference; the second phase is to generate the normalized linear octrees of the object and theseselected models and then to match the normalized linear octree of the unknown object with themodel having the minimum symmetric difference.

Facial Asymmetry Correction in Facial Palsy Patients with Silhouette Sutures

Introduction: over the last few decades several techniques static and dynamics, have been performed to improve facial asymmetry and functionality alter suffering facial paralysis. Methods: we present a pilot study to show and evaluate the benefits of a new form of facial suspension, with Silhouette sutures. We performed two patients with total and complete facial palsy due to otical tuberculosis in one case and to parotid carcinoma in the other. Results: one year after surgery, both patients have improved facial asymmetry, mastication and speech production which have lead to a higher self-esteem and major social interaction. Conclusions: static facial suspension with Silhouette sutures is a non invasive alternative to dynamic techniques in patients who don’t want or can’t undergo more complex surgeries.

An Imbalanced Data Classification Method Based on Hybrid Resampling and Fine Cost Sensitive Support Vector Machine

When building a classification model,the scenario where the samples of one class are significantly more than those of the other class is called data imbalance.Data imbalance causes the trained classification model to be in favor of the majority class(usually defined as the negative class),which may do harm to the accuracy of the minority class(usually defined as the positive class),and then lead to poor overall performance of the model.A method called MSHR-FCSSVM for solving imbalanced data classification is proposed in this article,which is based on a new hybrid resampling approach(MSHR)and a new fine cost-sensitive support vector machine(CS-SVM)classifier(FCSSVM).The MSHR measures the separability of each negative sample through its Silhouette value calculated by Mahalanobis distance between samples,based on which,the so-called pseudo-negative samples are screened out to generate new positive samples(over-sampling step)through linear interpolation and are deleted finally(under-sampling step).This approach replaces pseudo-negative samples with generated new positive samples one by one to clear up the inter-class overlap on the borderline,without changing the overall scale of the dataset.The FCSSVM is an improved version of the traditional CS-SVM.It considers influences of both the imbalance of sample number and the class distribution on classification simultaneously,and through finely tuning the class cost weights by using the efficient optimization algorithm based on the physical phenomenon of rime-ice(RIME)algorithm with cross-validation accuracy as the fitness function to accurately adjust the classification borderline.To verify the effectiveness of the proposed method,a series of experiments are carried out based on 20 imbalanced datasets including both mildly and extremely imbalanced datasets.The experimental results show that the MSHR-FCSSVM method performs better than the methods for comparison in most cases,and both the MSHR and the FCSSVM played significant roles.

Extracting Silhouette Curves of NURBS Surfaces by Tracing Silhouette Points

Extracting silhouette curves is a key part of hidden curve removal algorithms. It relates directly to the quality of graphics generated by the algorithm, and to the speed and space efficiency of the algorithm. This paper proposes an algorithm for extracting silhouette curves that numerically determines the starting and ending silhouette points, then traces each starting point to obtain the whole silhouette curve. Silhouette loops are obtained by tracing silhouette points on isoparametric lines. This algorithm is suitable for extracting silhouette curves at any given tolerance without surface subdivision.The algorithm is fast and requires minimal storage space.

Sports Events Recognition Using Multi Features and Deep Belief Network

In the modern era of a growing population,it is arduous for humans to monitor every aspect of sports,events occurring around us,and scenarios or conditions.This recognition of different types of sports and events has increasingly incorporated the use of machine learning and artificial intelligence.This research focuses on detecting and recognizing events in sequential photos characterized by several factors,including the size,location,and position of people’s body parts in those pictures,and the influence around those people.Common approaches utilized,here are feature descriptors such as MSER(Maximally Stable Extremal Regions),SIFT(Scale-Invariant Feature Transform),and DOF(degree of freedom)between the joint points are applied to the skeleton points.Moreover,for the same purposes,other features such as BRISK(Binary Robust Invariant Scalable Keypoints),ORB(Oriented FAST and Rotated BRIEF),and HOG(Histogram of Oriented Gradients)are applied on full body or silhouettes.The integration of these techniques increases the discriminative nature of characteristics retrieved in the identification process of the event,hence improving the efficiency and reliability of the entire procedure.These extracted features are passed to the early fusion and DBscan for feature fusion and optimization.Then deep belief,network is employed for recognition.Experimental results demonstrate a separate experiment’s detection average recognition rate of 87%in the HMDB51 video database and 89%in the YouTube database,showing a better perspective than the current methods in sports and event identification.

Probabilistic Model-Based Silhouette Refinement for Gait Recognition

An algorithm to refine and clean gait silhouette noises generated by imperfect motion detection techniques is developed,and a relatively complete and high quality silhouette is obtained.The silhouettes are sequentially refined in two levels according to two different probabilistic models.The first level is within-sequence refinement.Each silhouette in a particular sequence is refined by an individual model trained by the gait images from current sequence.The second level is between-sequence refinement.All the silhouettes that need further refinement are modified by a population model trained by the gait images chosen from a certain amount of pedestrians.The intention is to preserve the within-class similarity and to decrease the interaction between one class and others.Comparative experimental results indicate that the proposed algorithm is simple and quite effective,and it helps the existing recognition methods achieve a higher recognition performance.

Evaluating Partitioning Based Clustering Methods for Extended Non-negative Matrix Factorization (NMF)

Data is humongous today because of the extensive use of World WideWeb, Social Media and Intelligent Systems. This data can be very important anduseful if it is harnessed carefully and correctly. Useful information can beextracted from this massive data using the Data Mining process. The informationextracted can be used to make vital decisions in various industries. Clustering is avery popular Data Mining method which divides the data points into differentgroups such that all similar data points form a part of the same group. Clusteringmethods are of various types. Many parameters and indexes exist for the evaluationand comparison of these methods. In this paper, we have compared partitioningbased methods K-Means, Fuzzy C-Means (FCM), Partitioning AroundMedoids (PAM) and Clustering Large Application (CLARA) on secure perturbeddata. Comparison and identification has been done for the method which performsbetter for analyzing the data perturbed using Extended NMF on the basis of thevalues of various indexes like Dunn Index, Silhouette Index, Xie-Beni Indexand Davies-Bouldin Index.

Improved k-means clustering algorithm

In allusion to the disadvantage of having to obtain the number of clusters of data sets in advance and the sensitivity to selecting initial clustering centers in the k-means algorithm, an improved k-means clustering algorithm is proposed. First, the concept of a silhouette coefficient is introduced, and the optimal clustering number Kopt of a data set with unknown class information is confirmed by calculating the silhouette coefficient of objects in clusters under different K values. Then the distribution of the data set is obtained through hierarchical clustering and the initial clustering-centers are confirmed. Finally, the clustering is completed by the traditional k-means clustering. By the theoretical analysis, it is proved that the improved k-means clustering algorithm has proper computational complexity. The experimental results of IRIS testing data set show that the algorithm can distinguish different clusters reasonably and recognize the outliers efficiently, and the entropy generated by the algorithm is lower.

基于K均值聚类与随机森林算法的居民低碳出行意向数据挖掘

对居民低碳意识的形成机理进行研究,可以为交通管理者引导城市居民选择低碳出行方式提供重要依据.运用数据挖掘技术对低碳出行问卷数据进行分析;将计划行为理论框架下的15维问题视为表征居民低碳出行意愿的内在原因变量,应用K均值聚类算法对居民低碳出行意愿强度进行归类,并将所得结果作为被解释变量应用于随机森林模型中,探讨居民的社会属性特征、出行特征等对其低碳出行意愿的作用机理.结果表明:基于Silhouette指标检验及t-SNE降维,居民低碳出行意愿可划分为3类:强烈、中立、不强烈;基于重要性指标显示影响最为显著的4项因素分别是居民的职业、居住地、家庭构成、通勤时间.研究结果从多个角度为城市交通低碳化发展及管理提供政策建议.

基于流形结构邻域选择的局部投影近邻传播算法

近邻传播算法是一种快速有效的聚类方法.针对近邻传播算法在无先验知识条件下偏向参数选择的问题,使用Silhouette聚类有效性指标确定偏向参数.针对近邻传播算法在处理结构复杂或高维数据时,存在数据信息重叠的问题,提出将局部保持投影方法与近邻传播算法相结合的方法,在有效保留数据内部非线性结构的前提下,有效删除数据空间中的冗余信息.仿真结果验证了提出的算法优于传统的近邻传播算法.

基于有效性指标的聚类算法选择

为数据集选择合适的聚类算法是获得高质量聚类结果的前提和保障.提出了基于有效性指标的聚类算法选择方法,通过对不同聚类算法的聚类结果的质量评价为数据集选择最适合的聚类算法.该方法的优点是在对数据集的情况了解甚少的情况下,也能有效地保障聚类质量.实验结果表明本文方法十分有效,为实验数据集正确选择出最适合的聚类算法,并获得了高质量的聚类结果.

利用改进的最优聚类算法边缘提取方法研究

研究灰度图像的边缘提取的问题。针对传统边缘提取方法容易受到噪声干扰的问题,提出一种利用像素局部方差、信息熵、梯度和分散度特征的聚类算法,并利用Silhouette准则自动测定最优的聚类个数,从而有效地提高聚类和边缘提取的准确性。首先,利用对图像进行预处理,通过对各个像素提取四种不同的特征值,作为聚类分类器的输入;然后,遍历不同的聚类个数,并以Sil-houette作为最优聚类个数的判别标准,最终确定K聚类算法的类别个数。该方法可以有效地提取图像的边缘,尤其对噪声较多的图像能保证很好的边缘提取准确率。

欧盟法中的平行进口与商标权:历史演变与最新发展

一、引言 一国进口商在某一商标的商标权或商标使用一权已经受到本国法律保护的情况下,未经本国商标权所有人或许可使用人的许可,从国外购得相同商品重新输入本国的行为,相对于进口国的商标权人或许可使用人的出口或进口而言,就构成所谓的平行进口(parallel imports).