Robust uncalibrated stereo rectification with constrained geometric distortions (USR-CGD)

A novel algorithm for uncalibrated stereo image-pair rectification under the constraint of geometric distortion, called USR-CGD, is presented in this work. Although it is straightforward to define a rectifying transformation (or homography) given the epipolar geometry, many existing algorithms have unwanted geometric distortions as a side effect. To obtain rectified images with reduced geometric distortions while maintaining a small rectification error, we parameterize the homography by considering the influence of various kinds of geometric distortions. Next, we define several geometric measures and incorporate them into a new cost function as regularization terms for parameter optimization. Finally, we propose a constrained adaptive optimization scheme to allow a balanced performance between the rectification error and the geometric error. Extensive experimental results are provided to demonstrate the superb performance of the proposed USR-CGD method, which outperforms existing algorithms by a significant margin.     

基于匹配与平差的景象镶嵌方法

在概括介绍消附相邻景象辐射差异常用算法的基础上,为有效消除相邻景象间几何位置的差异,提出了一种将影象匹配与平差理论相结合的影象镶嵌新算法,该方法是在镶嵌影象的重叠部分进行基于特征的影象匹配,即首先获取一序列同名点,且这些同名点的匹配精度在一个象元以内;然后用正形多项式模拟这些同名点间的几何位置差异,再按最小二乘法原理对右边影象进行平差改正,以实现两影象空间位置上的无缝连接。实验结果表明,庐镶嵌方法具有良好的效果。     

基于仿射变换模型的图象特征点集配准方法研究

图象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象中寻找与模型图象的最佳匹配.目前,对于图象间的变换为相似变换的情形已有闭合公式.本文则分别运用最小二乘和矩阵伪逆两种方法,对图象间的变换为仿射变换的情形进行了研究,并给出了简单的闭合公式.实验表明这种方法精确、稳定、受噪声影响小.     

Restoring warped document images through 3D shape modeling

Scanning a document page from a thick bound volume often results in two kinds of distortions in the scanned image, i.e., shade along the "spine" of the book and warping in the shade area. In this paper, we propose an efficient restoration method based on the discovery of the 3D shape of a book surface from the shading information in a scanned document image. From a technical point of view, this shape from shading (SFS) problem in real-world environments is characterized by 1) a proximal and moving light source, 2) Lambertian reflection, 3) nonuniform albedo distribution, and 4) document skew. Taking all these factors into account, we first build practical models (consisting of a 3D geometric model and a 3D optical model) for the practical scanning conditions to reconstruct the 3D shape of the book surface. We next restore the scanned document image using this shape based on deshading and dewarping models. Finally, we evaluate the restoration results by comparing our estimated surface shape with the real shape as well as the OCR performance on original and restored document images. The results show that the geometric and photometric distortions are mostly removed and the OCR results are improved markedly.     

基于Cartesian矩的水印图像矫正算法

提出了一种水印图像矫正算法。该算法利用水印图像几何变换前后的Cartesian矩计算出水印图像缩放的尺度因子、旋转角度和平移参数,实现了对水印图像几何失真的快速矫正,不但可以校正水印图像的旋转失真、缩放失真和平移失真,还能有效地矫正联合失真。实验表明,该算法矫正精度高、性能稳定、计算量小,可以快速准确地矫正水印图像的高强度几何失真和联合失真。     

基于Hu不变矩和BP网络的条形码图像识别方法

针对目前比较流行的一维条形码和二维条形码识别算法存在对几何失真图像的识别准确率较低的问题,提出了一种新的基于不变矩和BP网络的条形码识别方法,提取不变矩特征向量作为特征值输入BP网络,对其进行训练与测试,利用训练好的BP网络对形变条形码图像进行识别,实现了对存在旋转、平移和缩放等几何失真的条形码图像的正确识别.实验结果表明,基于Hu不变矩和BP网络的条形码识别方法具有很强的抗图像平移、拉伸和旋转识别能力,并且具有实现简单、训练速度快、识别率高等特点.     

基于矩形角点几何变换的畸变图像校正

在以数码相机等数字设备拍摄图像时,所拍摄的图像经常会产生各种各样的变形,这种变形可能会导致识别软件中的后续处理失败,从而使图像无法被识别。为了使普通的识别软件能够对数码相机等拍摄的图像进行识别,有必要对其进行校正。针对大部分畸变图像是倾斜变形和透视变形,采用类似倾斜变形图像的几何校正算法,分两步实现图像的校正。实验表明,该方法对于倾斜变形和透视变形有良好的校正效果,以便于后续的图像处理。     

Coupled snakelets for curled text-line segmentation from warped document images

Camera-captured, warped document images usually contain curled text-lines because of distortions caused by camera perspective view and page curl. Warped document images can be transformed into planar document images for improving optical character recognition accuracy and human readability using monocular dewarping techniques. Curled text-lines segmentation is a crucial initial step for most of the monocular dewarping techniques. Existing curled text-line segmentation approaches are sensitive to geometric and perspective distortions. In this paper, we introduce a novel curled text-line segmentation algorithm by adapting active contour (snake). Our algorithm performs text-line segmentation by estimating pairs of x-line and baseline. It estimates a local pair of x-line and baseline on each connected component by jointly tracing top and bottom points of neighboring connected components, and finally each group of overlapping pairs is considered as a segmented text-line. Our algorithm has achieved curled text-line segmentation accuracy of above 95% on the DFKI-I (CBDAR 2007 dewarping contest) dataset, which is significantly better than previously reported results on this dataset.     

基于边缘特征的多源高分辨率影像配准

针对多源高分辨率影像之间较大的非线性辐射差异和局部几何变形造成较差配准精度的问题,提出一种基于边缘特征的多源高分辨率影像配准方法。该方法首先通过各向异性扩散滤波构造输入影像的非线性尺度空间,在此基础上计算每一尺度的扩展相位一致性最大矩以获取丰富的边缘特征,并利用基于分块策略的FAST检测器提取稳定的特征点;其次利用多尺度多方向LogGabor滤波生成主方向索引图（Main Orientated Index Map,MOIM）,并结合高斯加权构建一种稳健的特征描述子;最后采用巴氏距离和快速采样一致（Fast Sample Consensus,FSC）方法获取同名点。选择多组多源高分辨率影像进行实验,结果表明：该方法能够有效克服多源高分辨率影像间非线性辐射差异和局部几何变形,配准效果好于其他相关方法,并且平均配准精度优于1个像素。     

机载多光谱扫描图象几何畸变的全自动校正

分析了引起机载多光谱扫描图象几何畸变的各种主要因素，提出并建立了利用由惯性导航系统同步记录的飞机姿态、运动和位置参数校正机载多光谱扫描图象几何畸变的方法模型。试验结果表明，与传统采用控制点的方法相比，该方法不仅具有较高的校正精度，而且可由计算机全自动完成。     

简单人工辅助下的无限长遥感图像自动配准

遥感图像的配准是图像处理中的一个重要分支。部分遥感图像具有大尺度或无限长的特点,并且它们的失配是局部非线性的,直接进行通常意义上的全图配准很困难。该文假设这类图像的失配是连续变化的,建议了一种有初始人工辅助的自动流水线式的图像配准方法。该方法先在人工辅助下在某个初始区域建立初始匹配关系,然后从初始区域逐步扩散匹配控制点,网格约束下的控制点搜索匹配过程保证了在全图建立均匀的密度可控的控制点集。最后用基于多项式的局部加权平均算法完成图像的校正,这样可以保证对于无限长的图像以流水线的方式逐段配准输出。模拟试验结果证实了该方法的有效性。     

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.     

Geometrically invariant color image watermarking scheme using feature points

Geometric distortion is known as one of the most difficult attacks to resist. Geometric distortion desynchronizes the location of the watermark and hence causes incorrect watermark detection. In this paper,we propose a geometrically invariant digital watermarking method for color images. In order to synchronize the location for watermark insertion and detection,we use a multi-scale Harris-Laplace detector,by which feature points of a color image can be extracted that are invariant to geometric distortions. ...     

Document representation and its application to page decomposition

Transforming a paper document to its electronic version in a form suitable for efficient storage, retrieval, and interpretation continues to be a challenging problem. An efficient representation scheme for document images is necessary to solve this problem. Document representation involves techniques of thresholding, skew detection, geometric layout analysis, and logical layout analysis. The derived representation can then be used in document storage and retrieval. Page segmentation is an important stage in representing document images obtained by scanning journal pages. The performance of a document understanding system greatly depends on the correctness of page segmentation and labeling of different regions such as text, tables, images, drawings, and rulers. We use the traditional bottom-up approach based on the connected component extraction to efficiently implement page segmentation and region identification. A new document model which preserves top-down generation information is proposed based on which a document is logically represented for interactive editing, storage, retrieval, transfer, and logical analysis. Our algorithm has a high accuracy and takes approximately 1.4 seconds on a SGI Indy workstation for model creation, including orientation estimation, segmentation, and labeling (text, table, image, drawing, and ruler) for a 2550×3300 image of a typical journal page scanned at 300 dpi. This method is applicable to documents from various technical journals and can accommodate moderate amounts of skew and noise     

A self-adaptive correction method for perspective distortions of image

Frequently, the shooting angles available to photograph an object are limited, and the resultant images contain perspective distortions. These distortions make more difficult to perform subsequent tasks like feature extraction and information identification. This paper suggested a perspective correction method that extracts automatically distortion features through edge detection. Results showed that this method is powerful in correcting images with perspective distortions. The corrected image has virtually little information missing, clear features and high recovery rate.     

融合可变形卷积网络的鱼眼图像目标检测

环视鱼眼图像具有目标形变大和图像失真的缺点,导致传统网络结构在对鱼眼图像进行目标检测时效果不佳。为解决环视鱼眼图像中由于目标几何畸变而导致的目标检测难度大的问题,提出一种基于可变形卷积网络的鱼眼图像目标检测方法。将Cascade_RCNN中固定的卷积层和池化层分别替换为可变形卷积层和可变形池化层,使用Resnet50网络提取候选区域以获得检测框,级联具有不同IoU阈值的检测网络进行检测框抑制。在公开鱼眼图像数据集SFU_VOC_360和本文所采集的真实道路场景鱼眼图像数据集上进行实验,结果表明,该方法在鱼眼图像目标检测中具有有效性,目标检测准确率高于Cascade_RCNN网络。     

Projection‐type integral 3‐D display with distortion compensation

Abstract— Our research is aimed at developing a spatial‐imaging‐type integral three‐dimensional (3‐D) display based on an integral photography method using an extremely high‐resolution projector. One problem with the projection‐type integral 3‐D display is that geometrical distortion in projected elemental images causes spatial deformation of the displayed 3‐D image. In this study, a general relationship between the geometric distortion of elemental images and the spatial deformation of reconstructed 3‐D images were analyzed. A projection‐type integral 3‐D display with a distortion compensator which corrects the geometrical distortions of projected images in real‐time have been developed. The deformation of the displayed 3‐D images was significantly reduced by the distortion compensation, and the displayed 3‐D images had a resolution of 182 (H) × 140 (V) pixels and a viewing angle of 24.5°.     

基于小波提升框架的图像序列中运动目标检测算法

图像序列中运动目标的检测和跟踪是智能监测系统中的重要问题，为了提高运动目标识别的效率和准确性，同时解决日标运动中的形变造成的检测困难，首先针对传统的运动日标检测算法所存在的局限．把小波提升框架运用到运动目标的检测中，然后充分利用小波的多分辨率特性和提升框架可以直接在时空域内设计的优点，再结合可变模板方法，提出了一种新的基于小波提升框架的运动目标检测算法。实验结果表明，新方法较好地解决了目标运动中因形变造成的检测困难，并提高了检测效率和速度。     

Robust digital watermarking in PDTDFB domain based on least squares support vector machine

Geometric distortion is known as one of the most difficult attacks to resist, for it can desynchronize the location of the watermark and hence causes incorrect watermark detection. It is a challenging work to design a robust image watermarking scheme against geometric distortions. Based on the least squares support vector machine (LS-SVM) geometric distortions correction, we propose a new image watermarking scheme in shiftable complex directional pyramid (PDTDFB) domain with good visual quality and reasonable resistance toward geometric distortions in this paper. Firstly, the PDTDFB decomposition is performed on the original host image. Then, the corresponding lowpass subband is divided into small blocks. Finally, the digital watermark is embedded into host image by modulating the selected lowpass PDTDFB coefficients in small blocks. The main steps of digital watermark detecting procedure include: (1) the PDTDFB decomposition is performed on the test images, and some low-order Gaussian–Hermite moment energy of highpass subbands are computed, which are regarded as the effective feature vectors; (2) the appropriate kernel function is selected for training, and a LS-SVM training model can be obtained; (3) the watermarked image is corrected with the well trained LS-SVM model; and (4) the digital watermark is extracted from the corrected watermarked image. Experimental results show that the proposed image watermarking is not only invisible and robust against common image processing operations such as filtering, noise adding, and JPEG compression etc, but also robust against the geometrical distortions.     

Restoring 2D content from distorted documents

This paper presents a framework to restore the 2D content printed on documents in the presence of geometric distortion and non-uniform illumination. Compared with textbased document imaging approaches that correct distortion to a level necessary to obtain sufficiently readable text or to facilitate optical character recognition (OCR), our work targets nontextual documents where the original printed content is desired. To achieve this goal, our framework acquires a 3D scan of the document's surface together with a high-resolution image. Conformal mapping is used to rectify geometric distortion by mapping the 3D surface back to a plane while minimizing angular distortion. This conformal "deskewing" assumes no parametric model of the document's surface and is suitable for arbitrary distortions. Illumination correction is performed by using the 3D shape to distinguish content gradient edges from illumination gradient edges in the high-resolution image. Integration is performed using only the content edges to obtain a reflectance image with significantly less illumination artifacts. This approach makes no assumptions about light sources and their positions. The results from the geometric and photometric correction are combined to produce the final output.