Mean Shift迭代构造拓扑保持图像变换

在图像弹性配准中,基于径向基函数的小形变变换模型存在拓扑关系不能保持的问题.为此针对小形变模型提出一种基于Mean Shift迭代的拓扑保持图像变换方法.首先在拓扑不能保持的区域确定新增控制点对,通过Mean Shift迭代算法调整新增目标控制点的位置,再根据形变曲面的拓扑保持情况和配准度量的改善情况筛选新增控制点对,...     

自由曲面视觉测量标志点三维匹配方法研究

提出一种适用于自由曲面视觉测量的标志点三维匹配方法.该方法首先通过编码标志点确定局部测量区域在物体上的初始位置,获得可能匹配点集;然后根据非编码标志点与编码标志点之间的距离、角度、方向等特征,获得全局坐标系与局部坐标系之间的初始转换关系;最后根据此初始转换关系求解最终匹配点集,完成标志点的三维匹配.该方法适用于基于标志...     

基于标志点的测量数据自动拼接方法

为了对测量数据进行自动拼接,提出了一种新的数据拼接方法。该方法根据标志点的空间拓扑关系,先利用关系匹配自动找出对应的标志点对,以降低数据拼接的操作复杂性;同时应用Rodrigues参数表示位姿变换矩阵,并引入中间参数进行分步求解。数据拼接实例的拼接结果表明,该方法计算过程简单、稳定性好。     

图像扭曲变换中高斯基函数的最优参数分析

薄板样条插值TPS是基于标记点的医学图像弹性配准中常用的插值方法,但该方法的扭曲作用是全局的,对于局部扭曲配准会导致匹配精度下降。基于径向基函数的图像变换方法可以解决局部扭曲配准问题,但如何选取径向基函数的参数还没有很好地解决。本文针对以高斯基函数为径向基函数的局部弹性变换问题,讨论了双标志点情况下高斯径向基函数的参数选取方法,该方法可以使双标志点之间的扭曲影响范围最局部化。本文结论用于多标志点的图像配准时可以解决图像局部弹性变换问题,实验结果验证了本文的结论。     

基于小波变换的脉搏波标志点检测方法

针对脉搏波信号的标志点检测提出了一种基于小波变换的信号特征检测方法.该方法首先对脉搏波信号在小尺度上进行小波变换,然后利用系数中的极大极小值对来确定原始信号峰值的范围,进而返回原始信号定位峰值点.最后以原始信号的峰值为参照点提取出其他标志点的具体位置.采用此方法进行脉搏波信号的标志点定位,为进一步进行血流动力学参数的计算研究奠定了基础.     

一种环状编码标记点的设计及解码算法

针对近景摄影测量中对编码标志点的精确定位和准确识别的要求,提出一种环状编码标记点的设计和识别算法。在传统环状编码标记点的基础上添加3个定位符,用于确定标志点的精确位置和增加标志点的数量。解码时先检测定位符坐标及其在标志点中的位置,然后对编码标志点进行透视变换以实现图像校正的目的,最后用提出的基于圆环扫描的方法进行解码。实验结果表明,该算法对任意旋转角度下的编码标志点均能有较好的检测识别效果;当摄像机与标记平面的夹角小于65°时,其识别准确率可达99.3%;在复杂背景情况下的平均识别准确率为97.4%,误识别率为1.25%,识别平均速率为2.15 s/幅。     

基于标志点的三维点云自动拼接技术

为实现三维点云的自动拼接,提出一种标志点的三维点云自动拼接方法.根据标志点的空间特征不变性,匹配3个标志点,利用三点法求取坐标变换矩阵,对目标标志点集合进行坐标变换;采用k-d树搜索最接近标志点,设置距离阈值排除错误标志点对,通过哈希表替换坐标变换后的标志点;运用最小二乘法求解点云的变换矩阵,进行点云拼接.三维拼接实验结果表明,该方法拼接精度高、错误率低,能够实现快速、自动拼接.     

裁剪面的一类参数变换

给出了一类可以保持几何与拓扑信息一致性的裁剪面的参数变换定理及其算法。首先,确定了参数变换对裁剪面表示信息的影响。然后,根据参数变换后几何与拓扑信息的一致性要求,给出了对裁剪面表示信息进行调整的方法。最后,通过建立参数变换的关系,以裁剪球面为例阐述了这类参数变换的具体实现方法。     

基于多尺度空间划分与路网建模的城市移动轨迹模式挖掘

针对城市移动轨迹模式挖掘问题展开研究, 提出移动全局模式与移动过程模式相结合的挖掘方法, 即通过移动轨迹的起始位置点--终点位置点 (Origin-destination, OD点) 与移动过程序列分别进行移动全局模式与过程模式的发现. 在移动全局模式发现中, 提出了弹性多尺度空间划分方法, 避免了硬性等尺度网格划分对密集区域边缘的破坏, 同时增强了密集区域与稀疏区域的区分能力.在移动过程模式发现中, 提出了基于移动轨迹的路网拓扑关系模型构建方法, 通过路网关键位置点的探测抽取拓扑关系模型.最后基于空间划分集合与路网拓扑模型对原始 移动轨迹数据进行序列数据转换与频繁模式挖掘. 通过深圳市出租车历史 GPS 轨迹数据的实验结果表明, 该方法与现有方法相比在区域划分、数据转换等方面具有更好的性能, 同时挖掘结果语义更为丰富, 可解释性更强.     

基于最小二乘法的联合地图匹配算法的研究

地图匹配是车辆监控与管理系统的主要功能之一.从提高地图匹配算法准确率和缩短监控系统所需地图匹配时间的角度出发,提出了一种新的基于最小二乘法的联合地图匹配算法.该算法利用最小二乘法改进几何线到线地图匹配算法和拓扑结构地图匹配算法,根据路段混合使用算法,能实时准确地将行驶车辆匹配到相应的道路上.实验仿真表明,该算法为几何线到线在平行路段匹配不准的问题和拓扑范围越大匹配越不准的问题提供了有效的解决方法,具体可应用于路网密集的区域,为路网密集区域的道路定位与路径匹配提供准确和快捷的途径.     

3D Topology Preserving Flows for Viewpoint-Based Cortical Unfolding

We present a variational method for unfolding of the cortex based on a user-chosen point of view as an alternative to more traditional global flattening methods, which incur more distortion around the region of interest. Our approach involves three novel contributions. The first is an energy function and its corresponding gradient flow to measure the average visibility of a region of interest of a surface with respect to a given viewpoint. The second is an additional energy function and flow designed to preserve the 3D topology of the evolving surface. The third is a method that dramatically improves the computational speed of the 3D topology preservation approach by creating a tree structure of the 3D surface and using a recursion technique. Experiments results show that the proposed approach can successfully unfold highly convoluted surfaces such as the cortex while preserving their topology during the evolution.     

ℒ0 Gradient‐Preserving Color Transfer

This paper presents a new two‐step color transfer method which includes color mapping and detail preservation. To map source colors to target colors, which are from an image or palette, the proposed similarity‐preserving color mapping algorithm uses the similarities between pixel color and dominant colors as existing algorithms and emphasizes the similarities between source image pixel colors. Detail preservation is performed by an ?0 gradient‐preserving algorithm. It relaxes the large gradients of the sparse pixels along color region boundaries and preserves the small gradients of pixels within color regions. The proposed method preserves source image color similarity and image details well. Extensive experiments demonstrate that the proposed approach has achieved a state‐of‐art visual performance.     

Demons非刚性配准算法拓扑保持性的研究

在基于配准的图像分割应用中, 拓扑保持性是非刚性图像配准算法的一个重要约束. 本文从矢量场特性出发, 分析了Demons非刚性图像配准算法导致目标拓扑改变时变形场的特点. 根据变形场特点与其雅可比行列式之间的关系, 给出了校正该算法拓扑保持性的方法. 实验表明, 改进后的变形场具有了拓扑保持性.     

A combined measure for quantifying and qualifying the topology preservation of growing self-organizing maps

The Self-Organizing Map (SOM) is a neural network model that performs an ordered projection of a high dimensional input space in a low-dimensional topological structure. The process in which such mapping is formed is defined by the SOM algorithm, which is a competitive, unsupervised and nonparametric method, since it does not make any assumption about the input data distribution. The feature maps provided by this algorithm have been successfully applied for vector quantization, clustering and high dimensional data visualization processes. However, the initialization of the network topology and the selection of the SOM training parameters are two difficult tasks caused by the unknown distribution of the input signals. A misconfiguration of these parameters can generate a feature map of low-quality, so it is necessary to have some measure of the degree of adaptation of the SOM network to the input data model. The topology preservation is the most common concept used to implement this measure. Several qualitative and quantitative methods have been proposed for measuring the degree of SOM topology preservation, particularly using Kohonen's model. In this work, two methods for measuring the topology preservation of the Growing Cell Structures (GCSs) model are proposed: the topographic function and the topology preserving map.     

Topology preservation in self-organizing feature maps: exactdefinition and measurement

The neighborhood preservation of self-organizing feature maps like the Kohonen map is an important property which is exploited in many applications. However, if a dimensional conflict arises this property is lost. Various qualitative and quantitative approaches are known for measuring the degree of topology preservation. They are based on using the locations of the synaptic weight vectors. These approaches, however, may fail in case of nonlinear data manifolds. To overcome this problem, in this paper we present an approach which uses what we call the induced receptive fields for determining the degree of topology preservation. We first introduce a precise definition of topology preservation and then propose a tool for measuring it, the topographic function. The topographic function vanishes if and only if the map is topology preserving. We demonstrate the power of this tool for various examples of data manifolds.     

一种有效的隐私保护关联规则挖掘方法

隐私保护是当前数据挖掘领域中一个十分重要的研究问题,其目标是要在不精确访问真实原始数据的条件下,得到准确的模型和分析结果.为了提高对隐私数据的保护程度和挖掘结果的准确性,提出一种有效的隐私保护关联规则挖掘方法.首先将数据干扰和查询限制这两种隐私保护的基本策略相结合,提出了一种新的数据随机处理方法,即部分隐藏的随机化回答(randomized response with partial hiding,简称RRPH)方法,以对原始数据进行变换和隐藏.然后以此为基础,针对经过RRPH方法处理后的数据,给出了一种简单而又高效的频繁项集生成算法,进而实现了隐私保护的关联规则挖掘.理论分析和实验结果均表明,基于RRPH的隐私保护关联规则挖掘方法具有很好的隐私性、准确性、高效性和适用性.     

On Topology Preservation in 3D Thinning

Topology preservation is a major concern of parallel thinning algorithms for 2D and 3D binary images. To prove that a parallel thinning algorithm preserves topology, one must show that it preserves topology for all possible images. But it would be difficult to check all images, since there are too many possible images. Efficient sufficient conditions which can simplify such proofs for the 2D case were proposed by Ronse [Discrete Appl. Math. 21, 1988, 69-79]. By Ronse′s results, a 2D parallel thinning algorithm can be proved to be topology preserving by checking a rather small number of configurations. This paper establishes sufficient conditions for 3D parallel thinning algorithms to preserve topology.     

Topology Preserving Simplification of Medial Axes in 3D Models

We propose an efficient method for topology‐preserving simplification of medial axes of 3D models. Existing methods either cannot preserve the topology during medial axes simplification or have the problem of being geometrically inaccurate or computationally expensive. To tackle these issues, we restrict our topology‐checking to the areas around the topological holes to avoid unnecessary checks in other areas. Our algorithm can keep high precision even when the medial axis is simplified to be in very few vertices. Furthermore, we parallelize the medial axes simplification procedure to enhance the performance significantly. Experimental results show that our method can preserve the topology with highly efficient performance, much superior to the existing methods in terms of topology preservation, accuracy and performance.     

A directed batch growing approach to enhance the topology preservation of self-organizing map

The growing self-organizing map (GSOM) possesses effective capability to generate feature maps and visualizing high-dimensional data without pre-determining their size. Most of the proposed growing SOM algorithms use an incremental learning strategy. The conventional growing approach of GSOM is based on filling all available position around the candidate neuron which can decrease the topology preservation quality of the map due to the misconfiguration and twisting of the map which could be a consequence of unexpected network growth and improper neuron addition and weight initialization. To overcome this problem, in this paper we introduce a batch learning strategy for growing self-organizing maps called DBGSOM which direct the growing process based on the accumulative error around the candidate boundary neuron. In the proposed growing approach, just one new neuron is added around each candidate boundary neuron. The DBGSOM offers suitable mechanisms to find a proper growing positions and allocating initial weight vectors for the new neurons.The potential of the DBGSOM was investigated with one synthetic dataset and six real-world benchmark datasets in terms of topology preservation and mapping quality. Experimental results showed that the proposed growing strategy provides an enhanced topology preserved map and reduces the susceptibility of twisting compared to GSOM. Furthermore, the proposed method has a better clustering ability than GSOM and SOM. According to the lower number of neurons generated by DBGSOM, it needs less time to learn the manifold of the data points compared to GSOM.     

Detection of the non-topology preservation of Ma and Sonka’s algorithm,by the use of P-simple points

In 1996, Ma and Sonka proposed a thinning algorithm which yields curve skeletons for 3D binary images [C. Ma, M. Sonka, A fully parallel 3D thinning algorithm and its applications, Comput. Vis. Image Underst. 64 (3) (1996) 420–433]. This algorithm is one of the most referred thinning algorithms in the context of digital topology: either by its use in medical applications or for comparisons with other thinning algorithms.In 2007, Wang and Basu [T. Wang, A. Basu, A note on ‘a fully parallel 3D thinning algorithm and its applications’, Pattern Recognit. Lett. 28 (4) (2007) 501–506] wrote a paper in which they claim that Ma and Sonka’s 3D thinning algorithm does not preserve topology. As they highlight in their paper, a counter-example was given in 2001, in Lohou’s thesis [C. Lohou, Contribution à l’analyse topologique des images: étude d’algorithmes de squelettisation pour images 2D et 3D selon une approche topologie digitale ou topologie discrète. Ph.D. thesis, University of Marne-la-Vallée, France, 2001].In this paper, it is shown how P-simple points have guided the author towards a proof that Ma and Sonka’s algorithm does not always preserve topology. Moreover, the reasoning being very general, it could be reused for such a purpose, i.e., to simplify the proof on the non-topology preservation.