基于灰关联分析的遥感影像无缝拼接*

根据中心像元与周围像元之间的邻近像元效应,提出了一种基于灰关联分析的多景遥感影像无缝拼接方法。该方法以影像重叠区域中心像元邻域作为参考序列与比较序列,利用斜率关联度计算两者之间的灰关联度,找到一条影像色调和纹理差异较小的拼接线,并对其两侧作加权灰度平滑处理,以消除拼接图像之间因色调差异造成的接缝效应。实验结果表明,该方法算法简单,易于实现,影像拼接质量较好。     

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.     

An iterative point correspondence algorithm for automatic image registration: an application to dental subtraction radiography

In this paper, an Automatic Iterative Point Correspondence (AIPC) algorithm towards image registration is presented. Given an image pair, distinctive points are extracted only in one of the images (reference image), and the corresponding points in the other image are obtained automatically by maximizing a similarity measure between regions of the two images with respect to the parameters of a local transformation. The maximization is accomplished by means of an iterative procedure, in which candidate solutions for the transformation parameters are tested at each iteration; these solutions are evaluated by the similarity measure between image regions. The detected point pairs by the application of the AIPC algorithm are then used to estimate the parameters of a global projective transformation for the registration of the image pair. The proposed AIPC algorithm was applied on 113 in vitro and in vivo dental image pairs providing improved registration accuracy against three widely used registration methods.     

Differential Evolution as a viable tool for satellite image registration

A software system grounded on Differential Evolution to automatically register multiview and multitemporal images is designed, implemented and tested through a set of 2D satellite images on two problems, i.e. mosaicking and changes in time. Registration is effected by looking for the best affine transformation in terms of maximization of the mutual information between the first image and the transformation of the second one, and no control points are needed in this approach. This method is compared against five widely available tools, and its effectiveness is shown.     

基于多分辨率特征的图像镶嵌技术

图像镶嵌是IBR研究的热点技术之一,而其核心问题是图像配准。为了配准两帧图像,必须寻找两帧图像对之间的坐标变换。该文提出的基于多分辨率特征的方法有效地实现了图像间的8-参数投影变换模型的估计。该方法经测试效果良好。     

Texturing 3D Models of Real World Objects from Multiple Unregistered Photographic Views

As the efficiency of computer graphic rendering methods is increasing, generating realistic models is now becoming a limiting factor. In this paper we present a new technique to enhance already existing geometry models of real world objects with textures reconstructed from a sparse set of unregistered still photographs. The aim of the proposed technique is the generation of nearly photo-realistic models of arbitrarily shaped objects with minimal effort. In our approach, we require neither a prior calibration of the camera nor a high precision of the user's interaction. Two main problems have to be addressed of which the first is the recovery of the unknown positions and parameters of the camera. An initial estimate of the orientation is calculated from interactively selected point correspondences. Subsequently, the unknown parameters are accurately calculated by minimising a blend of objective functions in a 3D-2D projective registration approach. The key point of the proposed method of registration is a novel filtering approach which utilises the spatial information provided by the geometry model. Second, the individual images have to be combined yielding a set of consistent texture maps. We present a robust method to recover the texture from the photographs thereby preserving high spatial frequencies and eliminating artifacts, particularly specular highlights. Parts of the object not seen in any of the photographs are interpolated in the textured model. Results are shown for three complex example objects with different materials and numerous self-occlusions.     

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.     

Matching free stereovision for detecting obstacles on a ground plane

This paper presents a method for detecting obstacles on a ground plane from a stereo pair of images. Although we use stereovision, the obstacle detection algorithm relies neither on stereo matching nor 3D reconstruction. The principle here is to apply the projective transformation constraining the left and right images to obtain a frame of superimposed features (e.g. edges). By analysing feature superimposition after the projective transformation, a free moving space or space occupied by obstacles/occluded features can be determined.     

Mosaicking images [panoramic imaging]

Two methods of mosaicking images were developed, one for the simple case of a shift between two following images and another one for the general case. For the simple mosaicking, the error rate is acceptable for both cases of noisy or less noisy images. Owing to the fact that future generations of scouts would be provided with the 2.4 GHz transmission frequency, panoramic mosaics can be created with a high level of success. The operation is completely automated, and the execution time is close to three minutes for each mosaic. For the case of mosaicking images in general, the major problem is that the quality of the images given by the scout does not allow for choosing a small number of points for the Harris detector. This leads to long execution times. Furthermore, it is extremely hard to realize a panoramic vision by such a method because it is still unreliable without the intervention of an operator. Building 360/spl deg/ mosaics is not practical. Nevertheless, the general method can be used for creating precise mosaics of three consecutive images, thus providing wide-angle images. Image resulting from this operation represents 100/spl deg/ mosaics. Alas, the time of calculation is still a considerable four minutes.     

Efficient and Handy Texture Mapping on 3D Surfaces

There has been a rapid technical progress in three-dimensional (3D) computer graphics. But gathering surface and texture data is yet a laborious task. This paper addresses the problem of mapping photographic images on the surface of a 3D object whose geometric data are already known. We propose an efficient and handy method for acquiring textures and mapping them precisely on the surface, employing a digital camera alone. We describe an algorithm for selecting a minimal number of camera positions that can cover the entire surface of a given object and also an algorithm to determine camera's position and direction for each photograph taken so as to paste it to the corresponding surfaces precisely. We obtained a matching accuracy within a pixel on a surface through three experimental examples, by which the practicability of our method is demonstrated.     

遥感彩色影像镶嵌拼接缝的消除方法研究

对遥感影像拼接缝消除方法存在的问题进行了分析,提出了基于色度空间变换和动态宽度的遥感彩色图像拼接缝消除新方法。在HLS色彩空间,从色度、亮度和饱和度三个方面对拼接缝两侧像元值差异进行消除后,再转换为RGB彩色图像,并用4幅彩红外航空遥感图像进行了试验。研究结果表明,所提出的方法对于遥感彩色图像拼接缝消除具有较好的效果,算法简单、易于实现。     

Projective invariants of co-moments of 2D images

Functions of moments of 2D images that are invariant under some changes are important in image analysis and pattern recognition. One of the most basic changes to a 2D image is geometric change. Two images of the same plane taken from different viewpoints are related by a projective transformation. Unfortunately, it is well known that geometric moment invariants for projective transformations do not exist in general. Yet if we generalize the standard definition of the geometric moments and utilize some additional information from the images, certain type of projective invariants of 2D images can be derived. This paper first defines co-moment as a moment-like function of image that contains two reference points. Then a set of functions of co-moments that is invariant under general projective transformations is derived. The invariants are simple and in explicit form. Experimental results validated the mathematical derivations.     

A Linear Algorithm for Computing the Homography from Conics in Correspondence

This paper presents a study, based on conic correspondences, on the relationship between two perspective images acquired by an uncalibrated camera. We show that for a pair of corresponding conics, the parameters representing the conics satisfy a linear constraint. To be more specific, the parameters that represent a conic in one image are transformed by a five-dimensional projective transformation to the parameters that represent the corresponding conic in another image. We also show that this transformation is expressed as the symmetric component of the tensor product of the transformation based on point/line correspondences and itself. In addition, we present a linear algorithm for uniquely determining the corresponding point-based transformation from a given conic-based transformation up to a scale factor. Accordingly, conic correspondences enable us to easily handle both points and lines in uncalibrated images of a planar object.     

Theory and Practice of Projective Rectification

This paper gives a new method for image rectification, the process of resampling pairs of stereo images taken from widely differing viewpoints in order to produce a pair of matched epipolar projections. These are projections in which the epipolar lines run parallel with the x-axis and consequently, disparities between the images are in the x-direction only. The method is based on an examination of the fundamental matrix of Longuet-Higgins which describes the epipolar geometry of the image pair. The approach taken is consistent with that advocated by Faugeras (1992) of avoiding camera calibration. The paper uses methods of projective geometry to determine a pair of 2D projective transformations to be applied to the two images in order to match the epipolar lines. The advantages include the simplicity of the 2D projective transformation which allows very fast resampling as well as subsequent simplification in the identification of matched points and scene reconstruction.     

基于邻域传递的鱼眼图像的准稠密匹配

针对鱼眼图像高畸变的特点, 提出了一种适合于鱼眼图像的准稠密匹配扩散算法. 该算法采用局部仿射模型来建立图像对应区域之间的变换关系, 并利用这个仿射变换来规范化对应区域, 最后在规范化的区域上进行匹配扩散. 在局部仿射变换的计算中, 采用邻域传递的思路, 边扩散边邻域更新, 使扩散始终在较准确的对应区域内进行. 实验表明, 在大畸变的鱼眼图像下, 本文提出的准稠密扩散算法能够取得比较令人满意的匹配扩散结果.     

基于射影变换模型的图像特征点集配准

图像配准是计算机视觉中目标识别的一种基本方法,其目的是在待识别图像中寻找与模型图像的最佳匹配。该文讨论以特征点表示的图像间的配准问题,利用矩阵分解理论推导出射影变换下特征点集配准的闭合公式,给出变换参数估计的算法,并用模拟数据和图像角点检测的真实数据加以验证。实验表明该方法精确、稳定、受噪声影响小。     

空间约束的无人机影像SURF特征点匹配

与普通场景图像相比,无人机影像中纹理信息较丰富,局部特征与目标对象“一对多”的对应问题更加严重,经典SURF算法不适用于无人机影像的特征点匹配.为此,提出一种辅以空间约束的SURF特征点匹配方法,并应用于无人机影像拼接.该方法对基准影像整体提取SURF特征点,对目标影像分块提取SURF特征点,在特征点双向匹配过程中使用两特征点对进行空间约束,实现目标影像子图像与基准影像的特征点匹配;根据特征点对计算目标影像初始变换参数,估计目标影像特征点的匹配点在基准影像上的点位,对匹配点搜索空间进行约束,提高匹配速度与精度;利用点疏密度空间约束,得到均匀分布的特征点对.最后,利用所获取的特征点对实现无人机影像的配准与拼接,通过人工选取均匀分布的特征点对验证拼接精度.实验结果表明,采用本文方法提取的特征点能够得到较好的无人机影像拼接效果.     

Morphological image compositing

Image mosaicking can be defined as the registration of two or more images that are then combined into a single image. Once the images have been registered to a common coordinate system, the problem amounts to the definition of a selection rule to output a unique value for all those pixels that are present in more than one image. This process is known as image compositing. In this paper, we propose a compositing procedure based on mathematical morphology and its marker-controlled segmentation paradigm. Its scope is to position seams along salient image structures so as to diminish their visibility in the output mosaic even in the absence of radiometric corrections or blending procedures. We also show that it is suited to the seamless minimization of undesirable transient objects occurring in the regions where two or more images overlap. The proposed methodology and algorithms are illustrated for the composition of satellite images minimizing cloud cover.     

数字图像的半自动镶嵌方法

描述了一种实用的数字图像半自动镶嵌方法。这种方法简单有效,具有较高的几何配准精度以及较强的灰度调整和“接缝”消除能力,并通过使用半自动处理节省了大量镶嵌时间,提高了处理系统的性能。     

Synthesizing Novel Views from Unregistered 2-D Images

Synthesizing the image of a 3-D scene as it would be captured by a camera from an arbitrary viewpoint is a central problem in Computer Graphics. Given a complete 3-D model, it is possible to render the scene from any viewpoint. The construction of models is a tedious task. Here, we propose to bypass the model construction phase altogether, and to generate images of a 3-D scene from any novel viewpoint from prestored images. Unlike methods presented so far, we propose to completely avoid inferring and reasoning in 3-D by using projective invariants. These invariants are derived from corresponding points in the prestored images. The correspondences between features are established off-line in a semi-automated way. It is then possible to generate wireframe animation in real time on a standard computing platform. Well understood texture mapping methods can be applied to the wireframes to realistically render new images from the prestored ones. The method proposed here should allow the integration of computer generated and real imagery for applications such as walkthroughs in realistic virtual environments. We illustrate our approach on synthetic and real indoor and outdoor images.