Evaluation of Geometric Modeling for KOMPSAT‐1 EOC Imagery Using Ephemeris Data

Using stereo images with ephemeris data from the Korea Multi‐Purpose Satellite‐1 electro‐optical camera (KOMPSAT‐1 EOC), we performed geometric modeling for three‐dimensional (3‐D) positioning and evaluated its accuracy. In the geometric modeling procedures, we used ephemeris data included in the image header file to calculate the orbital parameters, sensor attitudes, and satellite position. An inconsistency between the time information of the ephemeris data and that of the center of the image frame was found, which caused a significant offset in satellite position. This time inconsistency was successfully adjusted. We modeled the actual satellite positions of the left and right images using only two ground control points and then achieved 3‐D positioning using the KOMPSAT‐1 EOC stereo images. The results show that the positioning accuracy was about 12‐17 m root mean square error (RMSE) when 6.6 m resolution EOC stereo images were used along with the ephemeris data and only two ground control points (GCPs). If more accurate ephemeris data are provided in the near future, then a more accurate 3‐D positioning will also be realized using only the EOC stereo images with ephemeris data and without the need for any GCPs.     

A contour-based approach to multisensor image registration

Image registration is concerned with the establishment of correspondence between images of the same scene. One challenging problem in this area is the registration of multispectral/multisensor images. In general, such images have different gray level characteristics, and simple techniques such as those based on area correlations cannot be applied directly. On the other hand, contours representing region boundaries are preserved in most cases. The authors present two contour-based methods which use region boundaries and other strong edges as matching primitives. The first contour matching algorithm is based on the chain-code correlation and other shape similarity criteria such as invariant moments. Closed contours and the salient segments along the open contours are matched separately. This method works well for image pairs in which the contour information is well preserved, such as the optical images from Landsat and Spot satellites. For the registration of the optical images with synthetic aperture radar (SAR) images, the authors propose an elastic contour matching scheme based on the active contour model. Using the contours from the optical image as the initial condition, accurate contour locations in the SAR image are obtained by applying the active contour model. Both contour matching methods are automatic and computationally quite efficient. Experimental results with various kinds of image data have verified the robustness of the algorithms, which have outperformed manual registration in terms of root mean square error at the control points.     

Urban object reconstruction using airborne laser elevation image and aerial image

Creating three-dimensional (3-D) models of real urban objects is an important goal in a wide variety of applications. This paper describes a method that utilizes airborne laser elevation images and aerial images for the 3-D reconstruction of urban objects. Our modeling approach uses the vertical geometric pattern analysis of elevation images. These patterns correspond to object contours and, thus, enable the extraction of the object. In addition, to provide realistic textured details, textures are cut from aerial images and mapped onto 3-D models. Our texture-mapping approach can avoid geometry mismatching and enable the automatic registration to determine the most reliable correspondence between projected outlines of 3-D models and contours of real objects shown in aerial images. Edge pairs, which are matched with projected outlines, are detected from aerial images. In order to minimize mismatching, we apply the voting technique based on the generalized Hough transform. Experimental results show that 3-D reconstruction of urban objects is generally successful.     

激光雷达与高光谱遥感对地立体探测研究

将激光雷达对大气,水体的透视及对地精确测距定位的立体成像探测能力和高光谱技术相结合,发展先进的空、天基对地立体成像综合定量探测系统,是国家对大气、陆地和海洋精确立体综合定量探测的迫切需要,也是遥感技术自身发展进步的要求,激光与地面、水体和大气的相互作用及其在高光谱的遥感响应是空,天基激光雷达和高光谱对地立体成像综合定量探测的核心理论基础和关键科学问题.综述了激光雷达对地探测系统的研究现状和发展趋势.提出了新的基于激光雷达与高光谱遥感的对地立体探测系统.将激光雷达对大气、水体的透视以及对地的精确测距定位与高光谱技术相结合,介绍了该系统的原理方法和特点.从激光雷达和高光谱数字影像的广义大气修正和地形辐射改正,大气光学厚度与程辐射图像的生成,激光雷达的非均匀水体修正和水底地形改正,激光雷达和高光谱与介质以及目标的相互作用,多源数据融合等五个方面阐述了该体系的关键技术及其研究思路.     

Optimized hierarchical block matching for fast and accurate image registration

Recently the camera resolution has been highly increased, and the registration between high-resolution images is computationally expensive even by using hierarchical block matching. This paper presents a novel optimized hierarchical block matching algorithm in which the computational cost is minimized for the scale factor and the number of levels in the hierarchy. The algorithm is based on a generalized version of the Gaussian pyramid and its inter-layer transformation of coordinates. The search window size is properly determined to resolve possible error propagation in hierarchical block matching. In addition, we also propose a simple but effective method for aligning colors between two images based on color distribution adjustment as a preprocessing. Simplifying a general color imaging model, we show much of the color inconsistency can be compensated by our color alignment method. The experimental results show that the optimized hierarchical block matching and color alignment methods increase the block matching speed and accuracy, and thus improve image registration. Using our algorithm, it takes about 1.28 s for overall registration process with a pair of images in 5 mega-pixel resolution.     

Adaptive image registration via hierarchical Voronoi subdivision

Advances in image acquisition systems have made it possible to capture high-resolution images of a scene, recording considerable scene details. With increased resolution comes increased image size and geometric difference between multiview images, complicating image registration. Through Voronoi subdivision, we subdivide large images into small corresponding regions, and by registering small regions, we register the images in a piecewise manner. Image subdivision reduces the geometric difference between regions that are registered and simplifies the correspondence process. The proposed method is a hierarchical one. While previous methods use the same block size and shape at a hierarchy, the proposed method adapts the block size and shape to the local image details and geometric difference between the images. This adaptation makes it possible to keep geometric difference between corresponding regions small and simplifies the correspondence process. Implementational details of the proposed image registration method are provided, and experimental results on various types of images are presented and analyzed.     

Surface-constrained volumetric brain registration using harmonic mappings

In order to compare anatomical and functional brain imaging data across subjects, the images must first be registered to a common coordinate system in which anatomical features are aligned. Intensity-based volume registration methods can align subcortical structures well, but the variability in sulcal folding patterns typically results in misalignment of the cortical surface. Conversely, surface-based registration using sulcal features can produce excellent cortical alignment but the mapping between brains is restricted to the cortical surface. Here we describe a method for volumetric registration that also produces an accurate one-to-one point correspondence between cortical surfaces. This is achieved by first parameterizing and aligning the cortical surfaces using sulcal landmarks. We then use a constrained harmonic mapping to extend this surface correspondence to the entire cortical volume. Finally, this mapping is refined using an intensity-based warp. We demonstrate the utility of the method by applying it to T1-weighted magnetic resonance images (MRIs). We evaluate the performance of our proposed method relative to existing methods that use only intensity information; for this comparison we compute the intersubject alignment of expert-labeled subcortical structures after registration.     

基于轮廓先验约束的复杂异形工件CT成像方法研究

在X射线CT成像检测系统中,复杂异形工件由于结构复杂,散射、硬化等现象较严重,而且在部分投影角度上,由于在射线透照方向上的有效厚度差异大,固定能量的射线剂量与厚度不匹配,投影数据质量较差,传统的CT重建算法得到的重建图像质量低,边缘模糊,无法获取工件的完整轮廓信息.为此文章研究了一种基于轮廓先验约束的复杂异形工件CT成像方法.首先利用双目立体视觉技术获取工件的轮廓信息,根据双目坐标系与CT坐标系间的空间位置关系完成先验图像的配准;然后将轮廓先验纳入到CT重建过程中并结合TV正则化进行轮廓约束重建.实验结果表明,该方法能够有效地抑制伪影和噪声,保留重建图像的边缘,改善重建图像质量,有助于提高复杂异形工件缺陷检测的可靠性.     

Image registration for image-based rendering.

Image-based rendering (IBR) has received much attention in recent years for its ability to synthesize photo-realistic novel views. To support translational motion, existing IBR methods either require a large amount of reference images or assume that some geometric information is available. However, rendering with a large amount of images is very expensive in terms of image acquisition, data storage, and memory costs. As IBR accepts various kinds of geometric proxy, we may use image registration techniques, such as stereo matching and structure and motion recognition, to obtain geometric information to help reduce the number of images required. Unfortunately, existing image registration techniques only support a small search range and require closely sampled reference images. This results in a high spatial sampling rate, making IBR impractical for use in scalable walkthrough environments.Our primary objective of this project is to develop an image registration technique that would recover the geometric proxy for IBR while, at the same time, reducing the number of reference images required. In this paper, we analyze the roles and requirements of an image registration technique for reducing the spatial sampling rate. Based on these requirements, we present a novel image registration technique to automatically recover the geometric proxy from reference images. With the distinguishing feature of supporting a large search range, the new method can accurately identify correspondences even though the reference images may only be sparsely sampled. This can significantly reduce the acquisition effort, the model size, and the memory cost.     

利用目标直线边缘交点的某型航侦CCD相机图像拼接

针对某型航侦CCD相机图像序列之间的特殊变换关系,提出了一种基于目标直线边缘交点的图像拼接方法。该方法在将相机图像重叠区域分割为较小独立区域的基础上,首先使用Canny边缘检测算子提取各个区域中的边缘信息,并用Hough变换提取边缘中的直线边缘。其次通过提出一种依据直线极坐标参数的区域扩展和选择策略来选择包含完整目标的子图像区域,并提取该区域中目标直线边缘的交点。最终根据提取到的直线边缘交点坐标实现图像配准,且采用双线性插值法融合配准区域,实现了图像的无缝拼接。实际的飞行效果验证了该图像拼接方法的有效性。     

基于边界距离场互信息的图像配准方法

基于图像边界平均Hausdorff距离的配准方法实现简单、速度快、有较大应用价值,但对图像边界不完全对应的情况配准效果不好。提出了一种以图像边界距离场互信息作为相似度函数的图像配准方法,以参考边界的距离场和浮动二值边界为两个离散概率分布,将其互信息作为相似度函数进行配准。实验结果表明,该算法对图像内容完全一致和内容不完全对应的图像均可得到良好的配准结果。     

A hybrid feature extraction method for SAR image registration

Extracting and matching correct correspondence between two images are significant stages for feature-based synthetic aperture radar (SAR) image registration. Two methods of feature extraction were employed in this study. Blob features were obtained by combining a Gaussian-guided filter (GGF) with a scale invariant feature transform, and corner features were obtained from the GGF. A GGF can store edge information and operate more effectively than a Gaussian filter. The ratio of average was used to compute gradients in order to reduce the speckle effect. Fast sample consensus (FSC) algorithm was combined with complete graph method for feature correspondence matching. Although FSC algorithm can extract valid correspondence, it may not be efficient enough to deal with SAR images due to its random nature and the large number of outliers in the data. Therefore, a graph-based algorithm was employed to solve the problem by eliminating outliers. The proposed hybrid method was tested on several real SAR images having different properties. The results showed that the proposed method performed the automated registration of SAR images more accurately and efficiently.     

Hole‐Filling Methods Using Depth and Color Information for Generating Multiview Images

This paper presents new hole‐filling methods for generating multiview images by using depth image based rendering (DIBR). Holes appear in a depth image captured from 3D sensors and in the multiview images rendered by DIBR. The holes are often found around the background regions of the images because the background is prone to occlusions by the foreground objects. Background‐oriented priority and gradient‐oriented priority are also introduced to find the order of hole‐filling after the DIBR process. In addition, to obtain a sample to fill the hole region, we propose the fusing of depth and color information to obtain a weighted sum of two patches for the depth (or rendered depth) images and a new distance measure to find the best‐matched patch for the rendered color images. The conventional method produces jagged edges and a blurry phenomenon in the final results, whereas the proposed method can minimize them, which is quite important for high fidelity in stereo imaging. The experimental results show that, by reducing these errors, the proposed methods can significantly improve the hole‐filling quality in the multiview images generated.     

Registration of stereo and temporal images of the retina

The registration of retinal images is required to facilitate the study of the optic nerve head and the retina. The method we propose combines the use of mutual information as the similarity measure and simulated annealing as the search technique. It is robust toward large transformations between the images and significant changes in light intensity. By using a pyramid sampling approach combined with simulated reannealing we find that registration can be achieved to predetermined precision, subject to choice of interpolation and the constraint of time. The algorithm was tested on 49 pairs of stereo images and 48 pairs of temporal images with success.     

平面图像立体化研究

将平面图像转换成立体图像很有意义。Hou等人(2002)提出了一种平面图像立体化方法,称为Hou方法。在这个方法中Hou等人使用随机变量等参数控制转换过程,但没有讨论这些参数对立体效果的影响。该文利用Hou方法在标准的计算机监视器上对含有心理深度暗示的平面图像进行了立体化,给出了评价转换后的立体效果的定量指标,并讨论了各参数对立体效果的影响。实验结果表明:当随机变量矩阵中的每一个随机变量都服从同一种均匀分布时,随机变量的取值对立体效果没有多大的影响;当监视器屏幕与观察者的距离为一个合适的值(例如1m)时,图像子块的个数取不同的值对立体效果没有多大的影响。     

一种新的双眼视觉的立体匹配方法及其应用

立体视觉中最大的困难是对应点的寻找,即需要匹配二幅或更多幅立体图像对之间的特征。本文提出一种新的双眼视觉的立体匹配方法,对积木(BLOCK)一类物体的立体图象匹配十分有效。将这种方法应用于三维物体的识别与定位,实验结果表明,与已有方法相比,本匹配方法简单、有效、匹配快速。文章最后给出用这种方法进行物体识别与精确定位的实验结果。     

Mean Shift的渐进无偏变换图像配准

基于标志点对应关系的图像配准方法不能保证变换的无偏性,一般不适用于大形变配准问题。该文提出了一种基于标志点对应关系的渐进式无偏变换图像配准方法,该方法利用少量的初始标志点对,通过Mean Shift迭代寻找对应标志点对,构造无偏性较好的变换函数,逐步得到一致的配准结果。文中详细讨论了标志点的分布对变换函数无偏性的影响,实验结果表明该文方法可以同时适用于大形变和小形变配准,是一种简单、有效的配准方法。     

Learning-based deformable registration of MR brain images

This paper presents a learning-based method for deformable registration of magnetic resonance (MR) brain images. There are two novelties in the proposed registration method. First, a set of best-scale geometric features are selected for each point in the brain, in order to facilitate correspondence detection during the registration procedure. This is achieved by optimizing an energy function that requires each point to have its best-scale geometric features consistent over the corresponding points in the training samples, and at the same time distinctive from those of nearby points in the neighborhood. Second, the active points used to drive the brain registration are hierarchically selected during the registration procedure, based on their saliency and consistency measures. That is, the image points with salient and consistent features (across different individuals) are considered for the initial registration of two images, while other less salient and consistent points join the registration procedure later. By incorporating these two novel strategies into the framework of the HAMMER registration algorithm, the registration accuracy has been improved according to the results on simulated brain data, and also visible improvement is observed particularly in the cortical regions of real brain data.     

Volume registration using needle paths and point landmarks for evaluation of interventional MRI treatments

We created a method for three-dimensional (3-D) registration of medical images (e.g., magnetic resonance imaging (MRI) or computed tomography) to images of physical tissue sections or to other medical images and evaluated its accuracy. Our method proved valuable for evaluation of animal model experiments on interventional-MRI guided thermal ablation and on a new localized drug delivery system. The method computes an optimum set of rigid body registration parameters by minimization of the Euclidean distances between automatically chosen correspondence points, along manually selected fiducial needle paths, and optional point landmarks, using the iterative closest point algorithm. For numerically simulated experiments, using two needle paths over a range of needle orientations, mean voxel displacement errors depended mostly on needle localization error when the angle between needles was at least 20 degrees. For parameters typical of our in vivo experiments, the mean voxel displacement error was < 0.35 mm. In addition, we determined that the distance objective function was a useful diagnostic for predicting registration quality. To evaluate the registration quality of physical specimens, we computed the misregistration for a needle not considered during the optimization procedure. We registered an ex vivo sheep brain MR volume with another MR volume and tissue section photographs, using various combinations of needle and point landmarks. Mean registration error was always < or = 0.54 mm for MR-to-MR registrations and < or = 0.52 mm for MR to tissue section registrations. We also applied the method to correlate MR volumes of radio-frequency induced thermal ablation lesions with actual tissue destruction. In this case, in vivo rabbit thigh volumes were registered to photographs of ex vivo tissue sections using two needle paths. Mean registration errors were between 0.7 and 1.36 mm over all rabbits, the largest error less than two MR voxel widths. We conclude that our method provides sufficient spatial correspondence to facilitate comparison of 3-D image data with data from gross pathology tissue sections and histology.     

A new validation method for X-ray mammogram registration algorithms using a projection model of breast X-ray compression

Establishing spatial correspondence between features visible in X-ray mammograms obtained at different times has great potential to aid assessment and quantitation of change in the breast indicative of malignancy. The literature contains numerous nonrigid registration algorithms developed for this purpose, but existing approaches are flawed by the assumption of inappropriate 2-D transformation models and quantitative estimation of registration accuracy is limited. In this paper, we describe a novel validation method which simulates plausible mammographic compressions of the breast using a magnetic resonance imaging (MRI) derived finite element model. By projecting the resulting known 3-D displacements into 2-D and generating pseudo-mammograms from these same compressed magnetic resonance (MR) volumes, we can generate convincing images with known 2-D displacements with which to validate a registration algorithm. We illustrate this approach by computing the accuracy for two conventional nonrigid 2-D registration algorithms applied to mammographic test images generated from three patient MR datasets. We show that the accuracy of these algorithms is close to the best achievable using a 2-D one-to-one correspondence model but that new algorithms incorporating more representative transformation models are required to achieve sufficiently accurate registrations for this application.