基于纹路的三维指纹模型重建算法

三维指纹识别是近几年兴起的一种基于三维指纹模型进行指纹识别的新技术,能够克服传统接触式指纹识别中存在的纹路变形、残留纹路、对手指皮肤状况敏感等缺陷。基于多角度图像的三维指纹模型重建是整个识别过程中的一个关键步骤。提出了一种基于纹路的重建算法,算法重建的指纹模型直接包含纹路与细节点相关特征。相对于已有文献中将指纹表皮作为重建对象,算法重建结果更有助于特征提取等三维指纹识别后续过程。     

Fingerprint warping using ridge curve correspondences

The performance of a fingerprint matching system is affected by the nonlinear deformation introduced in the fingerprint impression during image acquisition. This nonlinear deformation causes fingerprint features such as minutiae points and ridge curves to be distorted in a complex manner. A technique is presented to estimate the nonlinear distortion in fingerprint pairs based on ridge curve correspondences. The nonlinear distortion, represented using the thin-plate spline (TPS) function, aids in the estimation of an "average" deformation model for a specific finger when several impressions of that finger are available. The estimated average deformation is then utilized to distort the template fingerprint prior to matching it with an input fingerprint. The proposed deformation model based on ridge curves leads to a better alignment of two fingerprint images compared to a deformation model based on minutiae patterns. An index of deformation is proposed for selecting the "optimal" deformation model arising from multiple impressions associated with a finger. Results based on experimental data consisting of 1,600 fingerprints corresponding to 50 different fingers collected over a period of two weeks show that incorporating the proposed deformation model results in an improvement in the matching performance.     

A novel ant colony optimization algorithm for large-distorted fingerprint matching

Large distortion may be introduced by non-orthogonal finger pressure and 3D–2D mapping during the process of fingerprint capturing. Furthermore, large variations in resolution and geometric distortion may exist among the fingerprint images acquired from different types of sensors. This distortion greatly challenges the traditional minutiae-based fingerprint matching algorithms. In this paper, we propose a novel ant colony optimization algorithm to establish minutiae correspondences in large-distorted fingerprints. First, minutiae similarity is measured by local features, and an assignment graph is constructed by local search. Then, the minutiae correspondences are established by a pseudo-greedy rule and local propagation, and the pheromone matrix is updated by the local and global update rules. Finally, the minutiae correspondences that maximize the matching score are selected as the matching result. To compensate resolution difference of fingerprint images captured from disparate sensors, a common resolution method is adopted. The proposed method is tested on FVC2004 DB1 and a FINGERPASS cross-matching database established by our lab. The experimental results demonstrate that the proposed algorithm can effectively improve the performance of large-distorted fingerprint matching, especially for those fingerprint images acquired from different modes of acquisition.     

Fingerprint image mosaicking by recursive ridge mapping.

To obtain a large fingerprint image from several small partial images, mosaicking of fingerprint images has been recently researched. However, existing approaches cannot provide accurate transformations for mosaics when it comes to aligning images because of the plastic distortion that may occur due to the nonuniform contact between a finger and a sensor or the deficiency of the correspondences in the images. In this paper, we propose a new scheme for mosaicking fingerprint images, which iteratively matches ridges to overcome the deficiency of the correspondences and compensates for the amount of plastic distortion between two partial images by using a thin-plate spline model. The proposed method also effectively eliminates erroneous correspondences and decides how well the transformation is estimated by calculating the registration error with a normalized distance map. The proposed method consists of three phases: feature extraction, transform estimation, and mosaicking. Transform is initially estimated with matched minutia and the ridges attached to them. Unpaired ridges in the overlapping area between two images are iteratively matched by minimizing the registration error, which consists of the ridge matching error and the inverse consistency error. During the estimation, erroneous correspondences are eliminated by considering the geometric relationship between the correspondences and checking if the registration error is minimized or not. In our experiments, the proposed method was compared with three existing methods in terms of registration accuracy, image quality, minutia extraction rate, processing time, reject to fuse rate, and verification performance. The average registration error of the proposed method was less than three pixels, and the maximum error was not more than seven pixels. In a verification test, the equal error rate was reduced from 10% to 2.7% when five images were combined by our proposed method. The proposed method was superior to other compared methods in terms of registration accuracy, image quality, minutia extraction rate, and verification.     

Visual rendering of shapes on 2D display devices guided by hand gestures

Designing of touchless user interface is gaining popularity in various contexts. Users can interact with electronic devices using such interfaces even when their hands are dirty or non-conductive. Also, users with partial physical disability can interact with electronic devices with the help of touchless interfaces. In this paper, we propose a Leap Motion controller-based methodology to facilitate rendering of 2D and 3D shapes on display devices. The proposed method tracks finger movements while users perform natural gestures within the field of view of the motion sensor. Then, trajectories are analyzed to extract extended Npen++ features in 3D. These features capture finger movements during the gestures and they are fed to unidirectional left-to-right Hidden Markov Model (HMM) for training. A one-to-one mapping between gestures and shapes, is proposed. Finally, the shapes corresponding to these gestures are rendered over the display using a typical MuPad supported interface. We have created a dataset of 5400 samples recorded by 10 volunteers. Our dataset contains 18 geometric and 18 non-geometric shapes such as “circle”, “rectangle”, “flower”, “cone”, “sphere”, etc. The proposed method has achieved 92.87% accuracy using a 5-fold cross validation scheme. Experiments reveal that the extended 3D features perform better than the existing 3D features when applied for shape representation and classification. The method can be used for developing diverse HCI applications suitable for smart display devices.     

基于单目体系的可见手重构算法研究

首先确立单相机加单平面镜的体系结构,然后研究在该体系下实现三维重构的基本理论和基本方法,具体探讨了以下4个关键问题：（1）手边沿的提取;（2）对应关系的获取;（3）3D重构的基本方法;（4）校准算法,通过揭示出空间物点在像平面上的投影、该物点的对称点在同一像平面上的投影、镜面以及该物点本身这四者之间的关系,得到三维重构的新方法,既便于理论分析,又便于程序设计;既使校准过程简单易行,又保证了三维重构的精度．     

基于局部流形重构的半监督多视图图像分类

为了在半监督情境下利用多视图特征中的信息提升分类性能,通过最小化输入特征向量的局部重构误差为以输入特征向量为顶点构建的图学习合适的边权重,将其用于半监督学习。通过将最小化输入特征向量的局部重构误差捕获到的输入数据的流形结构应用于半监督学习,有利于提升半监督学习中标签预测的准确性。对于训练样本图像的多视图特征的使用问题,借助于改进的典型相关分析技术学习更具鉴别性的多视图特征,将其有效融合并用于图像分类任务。实验结果表明,该方法能够在半监督情境下充分地挖掘训练样本的多视图特征表示的鉴别信息,有效地完成鉴别任务。     

基于网格变形的从图像重建三维人脸

从图像重建高质量三维人脸一直是计算机视觉和图形学的一个重要研究问题.不同于传统的基于立体匹配的窄基线多视几何和数据驱动的人脸形变方法,提出一种结合网格变形技术和立体视觉原理的、从图像重建高质量三维人脸模型方法.给定从不同视角拍摄的几幅人脸图像,基于健壮图像特征获得可靠的相机外部参数和稀疏三维点;在此基础上,提出一种结合几何细节保持和图像一致性约束的三维人脸变形算法重建三维人脸,通过对人脸模板的网格变形,使得变形人脸在多幅图像中的可见投影具有一致性的图像颜色强度.基于模板的人脸变形可以有效地解决三维模型成像中的遮挡问题,采用健壮估计法消除噪声、离群点和光照对目标函数收敛性的影响,对目标函数的多次非线性优化求解进一步改进了人脸重建的质量.采用合成人脸图像和真实人脸图像重建三维人脸的实验结果表明,文中算法可以从几幅宽基线图像重建高质量的三维人脸模型.     

一种快速的三维扫描数据自动配准方法

研究了两幅和多幅深度图像的自动配准问题.在配准两幅深度图像时,结合二维纹理图像配准深度图像,具体过程是:首先,从扫描数据中提取纹理图像,特别地,针对不包含纹理图像的扫描数据提出了一种根据深度图像直接生成纹理图像的方法;然后,基于SIFT(scale-invariant feature transform)特征提取纹理图像中的兴趣像素,并通过预过滤和交叉检验兴趣像素等方法从中找出匹配像素对的候选集;之后,使用RANSAC(random sample consensus)算法,根据三维几何信息的约束找出候选集中正确的匹配像素对和相对应的匹配顶点对,并根据这些匹配顶点对计算出两幅深度图像间的刚体置换矩阵;最后,使用改进的ICP(iterative closest point)算法优化这一结果.在配准多幅深度图像时,提出了一种快速构建模型图的方法,可以避免对任意两幅深度图像作配准,提高了配准速度.该方法已成功应用于多种文物的三维逼真建模.     

Real-time 3D shape reconstruction, dynamic 3D mesh deformation, and high fidelity visualization for 3D video

3D video [IEEE Multimedia (1997) 18] is the ultimate image media recording dynamic visual events in the real world as is; it records time varying 3D object shape with high fidelity surface properties (i.e., color and texture). Its applications cover wide varieties of personal and social human activities: entertainment (e.g., 3D game and 3D TV), education (e.g., 3D animal picture books), sports (e.g., sport performance analysis), medicine (e.g., 3D surgery monitoring), culture (e.g., 3D archive of traditional dances), and so on. In this paper, we propose: (1) a PC cluster system for real-time reconstruction of dynamic 3D object action from multi-view video images, (2) a deformable 3D mesh model for reconstructing the accurate dynamic 3D object shape, and (3) an algorithm of rendering natural-looking texture on the 3D object surface from the multi-view video images. Experimental results with quantitative performance evaluations demonstrate the effectiveness of these methods in generating high fidelity 3D video from multi-view video images.     

Three-Dimensional Reconstruction of Points and Lines with Unknown Correspondence across Images

Three-dimensional reconstruction from a set of images is an important and difficult problem in computer vision. In this paper, we address the problem of determining image feature correspondences while simultaneously reconstructing the corresponding 3D features, given the camera poses of disparate monocular views. First, two new affinity measures are presented that capture the degree to which candidate features from different images consistently represent the projection of the same 3D point or 3D line. An affinity measure for point features in two different views is defined with respect to their distance from a hypothetical projected 3D pseudo-intersection point. Similarly, an affinity measure for 2D image line segments across three views is defined with respect to a 3D pseudo-intersection line. These affinity measures provide a foundation for determining unknown correspondences using weighted bipartite graphs representing candidate point and line matches across different images. As a result of this graph representation, a standard graph-theoretic algorithm can provide an optimal, simultaneous matching and triangulation of points across two views, and lines across three views. Experimental results on synthetic and real data demonstrate the effectiveness of the approach.An erratum to this article can be found at     

多层次感知的多视图三维模型重建

针对基于体素表征的多视图三维模型重建过程中,存在二维视图所包含的空间信息离散,空间网格中体素分布稀疏的问题,提出基于多层次感知的多视图三维模型重建方法,旨在通过对视图级、体素级与物体级信息的多层次感知,重建具有完整结构与局部细节的三维模型。在视图特征提取阶段设计了上下文感知的通道注意力模块来最大限度获取二维视图中潜在空间信息;在三维模型生成阶段,通过体素感知的VoxFocal Loss来促进空间网格中体素生成;在三维模型细化阶段,通过具有物体感知能力的三维判别器来自适应地消除三维模型中冗余体素,生成更具真实感的三维模型。在大型合成数据集ShapeNet和真实世界数据集Pix3D上验证了该方法的有效性与先进性。     

基于图割及动态片结构的3维人脸多视图体重建

针对3维人脸重建问题提出了一种新颖的多视图体重建方法,以解决目前3维人脸重建方法只适用于小样本集合,大范围推广时精度难以保证的弱点。该方法创新之处在于将基于特征点匹配的重建方法与立体重建方法结合引入到图割优化框架,并应用于3维人脸重建。本文两个重要改进工作是设计动态片结构描述来进行颜色一致性估计以及设计新的动态图结构以去除半个体素尺寸的重建误差。实验中分别采用8张、16张和30张存在亮度变化的人脸多视角图像验证算法。实验结果逼真,同时避免了传统重建方法结果受限于样本集分布的问题。     

直接提取指纹特征算法的优化研究

在对Maio与Maltoni于1997年提出的指纹特征灰度图直接提取算法进行分析的基础上，提出了一种改进的指纹特征提取算法，即在前景模板分割部分采用Canny算子和数学形态学相结合的方法；在终结点判定部分提出跟踪角度和灰度变化相结合的新方法；在滤波和去除伪节点等方面也进行了改进。实验证明，这些改进在很大程度上克服了原算法阈值过多，易提取出虚假脊线的问题。这样不仅使算法健壮性和准确性得到了很大的提高，而且更有利于实际应用。     

指纹图像融合迭代增强

从刑事现场采集的嫌疑人的指纹图像常是低质量的、残缺的,针对现有方法只做一次增强或不适合增强这类指纹图像,提出了指纹图像融合迭代增强方法。该方法分别在频域和空域两次增强,并采用小波图像融合技术进行迭代增强,有效地提高了脊线的清晰度,接续了脊线的断裂,修补了脊线的残缺。实验结果表明,该算法具有良好的指纹图像增强效果,适合增强低质量的指纹图像。     

Live-scanned fingerprint classification with Markov models modified by GA

Live-scanning devices are widely used in many fields. An important difference between fingerprint images acquired by ink and paper and fingerprints acquired by live-scanning devices is completeness. Since the sensor sizes of live-scanning devices are usually smaller than an average fingerprint and users may not align their fingers properly on the sensors, only a part of a fingerprint may be scanned, resulting in the omission of some singular points. In this paper, we propose a novel approach which increases the classification performance for fingerprint images obtained by live-scanning devices. We extract ridge directional values and create Markov models. However, Markov models in each class share most transitions because fingerprints are basically circular in shape. In order to enhance the specific transitions of each class and to suppress the common transitions in the Markov models, we apply genetic algorithms. The performance of the optimized classification model using genetic algorithms was shown to be superior to the pre-optimization model. The proposed method effectively classifies live-scanned fingerprint images because this approach is based on the global feature of ridge direction, and is independent of the existence of singular points.     

基于图像处理的外螺纹三维模型重构

提出了一种基于多角度序列图像特征实现外螺纹的三维模型重建的方法。 首先在旋转平台上采集多角度序列螺纹件图像,然后对每帧图像进行特征点提取,将序列图 像的特征点进行三维变换和插值,最终生成三维模型。实验结果表明,此算法能精确高精度 地实现外螺纹三维模型重构。     

一种指纹合成的方法及其实现

提出并实现了一种指纹合成方法.该方法通过新的联合方向场模型生成更加符合真实指纹的方向场,并提出一种新的指纹密度图的表示方法.然后,通过改进的Gabor滤波器生成指纹脊线纹理.该方法包括两个主要步骤:首先,经过指纹方向场生成、密度图生成和脊线纹理生成产生一个指纹模板图像;然后对指纹模板图像进行一系列变换,包括添加划痕、纹理平移、脊线的膨胀/腐蚀、脊线的弹性形变、脊线的加噪和平滑、图像的平移和旋转、改变对比度、添加背景噪声,最终得到类似于真实指纹的合成指纹图像.基于该方法的指纹生成器平台,已在2004年中国生