基于VFW的滚动按捺指纹拼接系统的设计

提出了一个基于VFW的滚动按捺指纹拼接系统的实现方案,通过自适应背景减除法实现了手指按下及抬起的自动检测,改进的序贯相似性检测法（SSDA）实现了指纹图像的配准。试验结果表明所述指纹采集方法优于平面按捺采集,对外界光照的变化、图像噪声具有很好的鲁棒性,在实验室的指纹建库中得到了很好的应用,有一定的实用价值。     

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.     

引入脊线结构信息的指纹匹配方法

提出了一种利用脊线和脊线结构信息进行指纹匹配的方法,即首先利用脊线的相似度初步确定若干参考点对,然后根据脊线间的结构信息利用矢量三角法对初步确定的参考点对进行筛选,最后用可变限界盒法进行匹配。实验结果表明,该方法能够准确地定位参考点对,快速正确地实现指纹匹配。     

Fingerprint classification by directional image partitioning

In this work, we introduce a new approach to automatic fingerprint classification. The directional image is partitioned into “homogeneous” connected regions according to the fingerprint topology, thus giving a synthetic representation which can be exploited as a basis for the classification. A set of dynamic masks, together with an optimization criterion, are used to guide the partitioning. The adaptation of the masks produces a numerical vector representing each fingerprint as a multidimensional point, which can be conceived as a continuous classification. Different search strategies are discussed to efficiently retrieve fingerprints both with continuous and exclusive classification. Experimental results have been given for the most commonly used fingerprint databases and the new method has been compared with other approaches known in the literature: As to fingerprint retrieval based on continuous classification, our method gives the best performance and exhibits a very high robustness     

基于Hermite滤波器的指纹增强算法

指纹增强的目的在于改善指纹图像的质量,以提高指纹识别系统的性能。提出了一种基于Hermite滤波器的指纹增强算法,算法利用经典的梯度公式法计算纹线方向,利用指纹图像子块的频谱分布特征计算纹线频率。以纹线方向和频率作为滤波器的主要参数,利用具有良好带通特性的Hermite滤波器和具有可变角度带宽的低通滤波器进行滤波,有效地提高指纹的纹理清晰度,较好地避免奇异点区域的块效应。实验结果表明,算法具有良好的图像增强效果。     

无人机遥感影像镶嵌技术综述

无人机影像镶嵌是低空遥感数据处理系统中的一个重要内容,其目的是将序列无人机影像拼接成一幅具有地理坐标的影像。为了使该领域的研究人员对当前无人机影像镶嵌方法有较全面的了解,对各种无人机影像镶嵌方法进行了综述。多项式法、卡尔曼滤波法、基于SfM点云匹配和传统空中三角测量法均需要地面控制点,而对偶四元数POS辅助空中三角测量方法无需或者只需少量地面控制点,将在无人机影像镶嵌领域有较为广阔的应用前景。     

A fast recursive mapping algorithm

The paper presents a generic technique for mapping parallel algorithms onto parallel architectures. The proposed technique is a fast recursive mapping algorithm which is a component of the Cluster-M programming tool. The other components of Cluster-M are the Specification module and the Representation module. In the Specification module, for a given task specified by a high-level machine-independent program, a clustered task graph called Spec graph is generated. In the Representation module, for a given architecture or computing organization, a clustered system graph called Rep graph is generated. Given a task (or system) graph, a Spec (or Rep) graph can be generated using one of the clustering algorithms presented in the paper. The clustering is done only once for a given task graph (system graph) independent of any system graphs (task graphs). It is a machine-independent (application-independent) clustering, and therefore it is not repeated for different mappings. The Cluster-M mapping algorithm presented produces a sub-optimal matching of a given Spec graph containing M task modules, onto a Rep graph of N processors, in O(MN) time. This generic algorithm is suitable for both the allocation problem and the scheduling problem. Its performance is compared to other leading techniques. We show that Cluster-M produces better or similar results in significantly less time and using fewer or an equal number of processors as compared to the other known methods.     

Fingerprint orientation field estimation using ridge projection

Orientation field plays an important role in fingerprint recognition system. This paper proposes a method for estimating four-direction orientation field. It consists of four steps: (i) preprocessing fingerprint image, (ii) determining the primary ridge of fingerprint block using neuron pulses of pulse coupled neural network (PCNN), (iii) estimating block direction by projective distance variance of such a ridge, instead of a full block and (iv) correcting the estimated orientation field. Experiments on the databases of FVC2004 show that the proposed method achieves good estimation accuracy with low computational time expense. In particular, this method shows good robustness to noise, and it can be integrated into a fingerprint recognition system to improve the performance.     

指纹图像的预处理

采用基于方向信息的形态学运算对指纹图像进行预处理。针对指纹图像的特点,首先对图像进行规格化;然后采用分块直方图均衡化方法做一次增强;接着用动态阈值法对图像二值化;根据方向信息对二值图像做基于形态学闭运算的二次增强;最后对二次增强图像细化。给出了一种简单有效的指纹图像预处理方法以及该方法的实验结果。     

无人机载荷图像地理信息拼接及验证算法

In the traditional video and image processing technology field, researchers often focus on the processing of the image content, especially the accuracy and the speed. However, the geographic information data carried by UAV video and image is often ignored, resulting in the image only containing the scene information and losing the geographic data information after the image processing iscomplet- ed, so that the user cannot quickly obtain the geographic information of the target of interest from image processing results. In order to process the geographic information efficiently, an image mosaicking and verification algorithm for UAV image with geographic information regards geographic information data as multi-channel double floating-point matrix data, which can be calculated synchronously using matrix processing algorithms. Meanwhile, the accuracy and speed of a large number of image mosaicking tasks can be improved by using image-splicing algorithm based on grouping control with geographic information. The experimental results show that the proposed algorithm can effectively process the UAV image with geographic information, especially in image mosaicking.     

混合递归超分辨率图像重构ZHANGDi,PENGHong.Hybridrecursivesuper-

超分辨率图像重构是将多帧低分辨率图像重构成一幅高分辨率图像的过程。一般的超分辨率图像重构算法在小放大倍数时效果比较好,但当放大倍数上升时,存在两个问题:一个是计算复杂性的急剧上升,另一个是边缘振荡效应的加剧导致成像质量迅速恶化。提出了一个能同时解决这两个问题的基于POCS的混合递归重构算法,实验结果表明,该算法能够在提高重构图像质量的同时,较大地提高重构速度。     

Aerial image mosaicking based on the 6-DoF imaging model

ABSTRACT

Image mosaicking technology is widely used in remote sensing and imaging of unmanned aerial vehicles (UAVs) for its superiority of generating large seamless images with high resolutions. Existing methods generally adopt the 8-DoF (degrees of freedom) homography camera model for image mosaicking, which require large computation cost in parameter optimization and cause distortions in the mosaicked images because of the accumulation errors. In view of this, this paper introduces an efficient mosaicking method based on 6-DoF imaging model to improve the quality of aerial image mosaicking. This derived model reduces the number of unknown variables for parameter optimization, hence improves the time efficiency. Meanwhile, a constraint term is merged into the objective function of optimization by analysing the characteristic of UAV imaging to eliminate the accumulation error in the mosaicking process. The experimental results on multiple datasets demonstrate that the proposed method achieves more accurate mosaicking results efficiently and is robust to different reference images relative to the state-of-the-art method.     

基于改进SIFT特征的水下图像拼接技术研究

针对水下图像存在颜色失真、对比度较低等问题,而传统的图像拼接算法直接用于水下图像拼接是不奏效的,文中选用暗通道先验去雾算法来对水下图像进行增强,再运用白平衡算法来校正水下图像的颜色,并用主观评价和客观评价来判断预处理的好坏。然后,选取尺度不变特征变换(SIFT)算法来进行特征点的提取。由于水下图像纹理信息较丰富,因此在进行特征点描述时添加图像的纹理信息,使其能够进行更精确的特征点描述。实验结果表明,改进后的算法能够降低误匹配率,改善水下图像拼接效果。     

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。     

Three-dimensional image mosaicking using multiple projection planes for 3-D visualization of roadside standing buildings.

A novel image-mosaicking technique suitable for 3-D visualization of roadside buildings on websites or mobile systems is proposed. Our method was tested on a roadside building scene taken using a side-looking video camera employing a continuous set of vertical-textured planar faces. A vertical plane approximation of the scene geometry for each frame was calculated using sparsely distributed feature points that were assigned 3-D data through bundle adjustments. These vertical planes were concatenated to create an approximate model on which the images could be backprojected as textures and blended together. Additionally, our proposed method includes an expanded crossed-slits projection around far-range areas to reduce the "ghost effect," a phenomenon in which a particular object appears repeatedly in a created image mosaic. The final step was to produce seamless image mosaics using Dijkstra's algorithm to find the optimum seam line to blend overlapping images. We used our algorithm to create efficient image mosaics in 3-D space from a sequence of real images.     

Optimal seamline detection in dynamic scenes via graph cuts for image mosaicking

In this paper, we present a novel method for creating a seamless mosaic from a set of geometrically aligned images captured from the scene with dynamic objects at different times. The artifacts caused by dynamic objects and geometric misalignments can be effectively concealed in our proposed seamline detection algorithm. In addition, we simultaneously compensate the image regions of dynamic objects based on the optimal seamline detection in the graph cuts energy minimization framework and create the mosaic with a relatively clean background. To ensure the high quality of the optimal seamline, the energy functions adopted in graph cuts combine the pixel-level similarities of image characteristics, including intensity and gradient, and the texture complexity. To successfully compensate the image regions covered by dynamic objects for creating a mosaic with a relatively clean background, we initially detect them in overlap regions between images based on pixel-level and region-level similarities, then refine them based on segments, and determine their image source in probability based on contour matching. We finally integrate all of these into the energy minimization framework to detect optimal seamlines. Experimental results on different dynamic scenes demonstrate that our proposed method is capable of generating high-quality mosaics with relatively clean backgrounds based on the detected optimal seamlines.     

小波域中的PDE指纹图像增强

提出了一种基于偏微分方程的小波域指纹增强算法。分析了一致性扩散方程的缺陷,改为使用方向扩散使方程严格沿着指纹纹路扩散;进而提出了一种基于方向扩散的小波域指纹增强算法。该算法利用小波域低频系数图增强指纹,抑制了噪声的影响,将增强子图利用小波逆变换实现重构。通过对FVC2002的dB4指纹库中部分低质量图像的增强结果比较,表明该算法对低质量指纹图像的增强效果明显,且处理速度比现存的基于PDE的增强方法快。     

An improved approach for generating globally consistent seamline networks for aerial image mosaicking

Seamless image mosaicking is a crucial technique in the production of orthophotos in photogrammetry and remote sensing. There are many studies focused on generating high-quality ortho-mosaics and many algorithms have been put forward for this purpose. Some of the existing methods concentrate on guiding the seamline to bypass ground objects, while others focus on generating a seamline network with better global quality, based on a regular and consistent initial network. However, the former may result in an irregular seamline network structure and the latter cannot guarantee the seamline bypasses ground objects in some cases, due to limited optimization. In this study, an improved image mosaicking approach, taking both the local optimization of a single seamline and the global regularity and consistency of the whole seamline network into consideration, is proposed in order to generate a globally consistent seamline network for aerial images. Firstly, the seamline is selected using a coarse-to-fine strategy between adjacent overlapping orthoimages, in which the initial seamline is generated based on our previous work and further refined based on texture difference. Then, an effective mosaic polygon of each orthoimage is obtained through a novel method, before being used to compose a globally consistent seamline network. Furthermore, an evaluation index called the Area Weighted Distance is introduced to quantitatively evaluate the global consistency and regularity of the generated seamline network. Experiment results demonstrate that the proposed approach is feasible and promising in terms of its performance. Compared to the comparison methods, the proposed approach is 1.35 and 2.15 times smaller in the average and standard deviation of the area weighted distance, respectively, and 3.6–6.5 times faster in terms of efficiency.     

Fingerprint image analysis for automatic identification

Most of the papers on fingerprints deal with classification of fingerprint images. Fingerprint databases being large (in the range of millions), the effort in matching of fingerprints within a class or when the class is unknown, is very significant. This requires fingerprint image analysis and extraction of the “minutiae” features, which are used for matching FPs. In this paper a scheme of preprocessing and feature extraction of fingerprint images for automatic identification is presented, which works even if the pattern class is unknown. The identification of fingerprints is based on matching the minutiae features of a given finger-print against those stored in the database. The core and delta information is used for classification and for registration while matching. These algorithms have been tested for more than 10,000 fingerprint images of different qualities. The results are manually verified and found to be very good for practical application. A few sample results are presented.