基于平面激光测量的移动机器人自定位方法

提出了两种基于平面激光测量的移动机器人自定位方法. 第一种方法是改进的Hough 密度谱的方法, 它的主要优点是避免了现有方法Hough 变换离散化过程中的信息损失问题，提高了算法的精度和鲁棒性. 该方法在引进一种新的Hough 密度谱的基础上，根据谱相关函数值和运动参数的密度得到机器人运动参数的候选值，并应用Hausdor 相似性度量从候选值确定运动参数的最终估计. 第二种方法是基于Fourier-Mellin 变换的方法，主要利用Fourier 变换的位移理论和Fourier-Mellin不变量来估计运动参数. 为了避免图像离散化造成的信息损失，在该方法中使用基于Hausdor 距离的最近点迭代(ICP) 算法来进一步精化平移向量. 实验结果表明，这两种方法均可有效地提高机器人的定位精度，具有一定的实际应用价值.     

基于Fourier-Mellin变换的图像配准算法及性能研究

快速傅立叶变换(FFT)改进了离散傅立叶变换(DFT)的计箅过程,被广泛应用于数字图像的实时处理中.在相位相关技术的基础上,提出了一种新的图像配准算法,即在需要配准的两幅图像中心选取相同区域大小,进行Fourier-Mellin变换,变换后是一个二维脉冲信号,由此而得到图像配准参数.实验结果表明了该算法的有效性和可靠性.     

Fourier-Mellin变换不同时相遥感影像自动配准研究

多时相遥感影像配准是变化检测的关键步骤。由于不同时相的遥感影像差异,且在传感器参数未知情况下,很难完成其自动配准。基于傅里叶-梅林变换（Fourier-Mellin Transform,FMT）影像配准其实就是基于傅里叶变换和对数极变换的全局相位相关。这种方法在进行频域计算时找到了配准的变换参数,并且对噪声和遮挡等很鲁棒。提出了一种基于Fourier-Mellin算法的改进配准方法。Fourier-Mellin变换由于旋转的频谱混叠和旋转变换中插值误差而产生错误。为了得到更好的配准结果,通过加窗和滤波来提高峰值、减少频谱混叠、增加鲁棒性。     

基于相位相关的图像匹配算法研究

提出一种基于相位相关的图像匹配方法.针对仅有位移变换的图像,给出基于相位相关的模板匹配方法,并进行了改进,然后利用人工平移的方式进行实验验证.结合Fourier-Mellin变换理论,给出解决旋转问题的图像匹配方法,并利用人工旋转的方式进行了实验验证.实验结果表明,本方法在精度和速度上都能取得比较满意的效果.     

局部相位相关用于图像亚像素级配准技术研究

提出了一种基于局部相位相关的高效和鲁棒的亚像素级图像配准方法。通过传统的相位相关算法估计出初始平移参数后,在初始位置的引导下对互相关功率谱进行上采样矩阵Fourier变换,实现了图像局部相位相关,得到图像间亚像素级平移参数。实验结果表明,算法配准精度较高,且对随机噪声和光照变化具有较强的鲁棒性。     

改进的基于小波变换的图像配准方法

针对如何提高图像配准的精度和速度的问题,利用基于Fourier-Mellin变换求解水平参量的准确性和基于互信息方法求解旋转及尺度变换参量准确性的优点,提出一种基于Fourier-Mellin和互信息的图像配准金字塔方法。对多幅遥感图像的实例仿真结果表明,该方法能在提高配准精度的同时减少配准时间。     

一种基于离散傅立叶变换域相位和幅度的数字水印算法

将水印分别嵌入离散傅立叶变换(DFT)域的相位谱、幅度谱进行仿真实验,比较了两种算法在相同的嵌入失真下的鲁棒性,提出了相位幅度相结合的DFT域水印算法,算法在提取水印时不需要原始图像。实验证明,该相幅水印算法简单易行,具有很好的不觉察性,而且对图像的尺度变换、旋转攻击、噪声干扰、JPEG压缩以及滤波等都具有很强的鲁棒性。     

基于相位相关法的全局运动估计算法

提出了一种基于鲁捧统计和相位相关法相结合的全局运动估计算法;由于相位相关法利用图像的功率谱信息,减少了对图像内容的依赖,具有一定的抗噪能力,因此该算法将块匹配法与相位相关相结合来计算图像间的运动矢量场,不仅减少了运算量而且能得到更加准确的矢量场;为了提高模型参数估计精度和运算效率,运用多分辨率鲁棒统计的方法来计算运动估计模型参数;航拍视频图像配准与独立运动检测的仿真结果均验证了算法的有效性。     

RDWT域全相位子带运动补偿视频编码方法

当前小波视频编码技术的一个研究热点是如何提高运动补偿的效率．提出了一种基于RDWT（冗余离散小波变换）域的奎相位子带运动补偿视频编码方法．运动补偿过程在参考帧和当前帧的RDWT域进行，使用提出的RDWT小波块结构，利用RDWT域的全相位子带信息提高运动补偿效率．在RDWT域形成预测数据后变换到空域得到预测帧和残差帧，对残差帕进行DWT变换，SPECK小波系数编码．实验表明提出自寺方法获得了较好的编码效率．     

基于Radon和解析Fourier-Mellin变换的尺度与旋转不变目标识别算法

由于正交矩对噪声鲁棒性强、重建效果好,因此被广泛应用于目标识别与分类中,但是正交矩本质上缺乏尺度变换不变性,而且必要的图像二值化与规一化过程会引入重采样与重量化误差。为此,在研究现有正交矩的基础上,提出了一种基于Radon变换和解析Fourier-Mellin变换的尺度与旋转不变的目标识别算法。该算法首先直接对目标灰度图像进行Radon变换,然后对Radon变换结果进行进一步解析,通过Fourier-Mellin变换将原图像的旋转变化转化为相位变化,将原图像的尺度变化转化为幅度变化;最后,通过定义一旋转与尺度不变函数,同时利用不变函数的4种特征,再应用k-近邻法实现分类。理论与实验结果表明,由于避免了正交矩方法存在的重采样与重量化误差,该算法的分类精度高于基于正交矩的分类方法,而且对白噪声的鲁棒性也显著高于基于正交矩的识别与分类方法。     

基于多帧融合及四参数仿射模型的图像超分辨

运动参数估计和复原是多帧图像超分辨重构中最重要的两个环节,其中经典的Fourier-Mellin变换方法于频域采用对数极坐标形式和相位相关方法结合来估计运动参数。相位相关是整像素级平移参数估计方法,将其改进为亚像素级平移参数估计方法,以提高旋转、缩放参数的估计精度。对于复原算法,在讨论基于局部信息的传统双三次插值超分辨重构方法的基础上,重点探讨基于全局信息的Kriging插值超分辨重构和核非线性回归（KNR）超分辨重构方法。实验结果表明,探讨的参数估计方法和超分辨重构方法是有效的。     

基于四元数傅里叶梅林变换的旋转不变彩色纹理分类

提出一种基于四元数傅里叶梅林变换(Quaternion Fourier-Mellin Transform,QFMT)的旋转不变彩色纹理分类方法。该方法首先对彩色图像各分量图像进行对数极坐标变换,然后将经过变换后的3幅分量图像表示成四元数,并对其进行四元数傅里叶变换(Quaternion Fourier Transform,QFT),最后对幅度谱分别统计其环形特征量和楔形特征量作为纹理分类的特征向量,利用最近邻分类器进行分类。实验结果表明,本文提出的方法分类准确率更高,且具有良好的旋转不变纹理分析性能。     

Hierarchical kernel-based rotation and scale invariant similarity

Image similarity measure has been widely used in pattern recognition and computer vision. We usually face challenges in terms of rotation and scale changes. In order to overcome these problems, an effective similarity measure which is invariant to rotation and scale is proposed in this paper. Firstly, the proposed method applies the log-polar transform to eliminate the rotation and scale effect and produces a row and column translated log-polar image. Then the obtained log-polar image is passed to hierarchical kernels to eliminate the row and column translation effects. In this way, the output of the proposed method is invariant to rotation and scale. The theoretical analysis of invariance has also been given. In addition, an effective template sets construction method is presented to reduce computational complexity and to improve the accuracy of the proposed similarity measure. Through the experiments with several image data sets we demonstrate the advantages of the proposed approach: high classification accuracy and fast.     

基于子像素的对数极坐标反向变换研究

对数极坐标变换算法不仅是对人眼视觉系统中视网膜视皮层映射的数学描述,而且也是空间变分辨率理论的重要算法。映射变换中距离上的对数运算与角度上的反正切运算导致变换阵坐标存在小数,并且值域区间过于狭窄。针对这个问题,文章提出了子像素反向变换算法,新算法下的变换阵图像具有良好的连续性,同时减弱了马赛克现象。     

A novel quantum representation for log-polar images

The power of quantum mechanics has been extensively exploited to meet the high computational requirement of classical image processing. However, existing quantum image models can only represent the images sampled in Cartesian coordinates. In this paper, quantum log-polar image (QUALPI), a novel quantum image representation is proposed for the storage and processing of images sampled in log-polar coordinates. In QUALPI, all the pixels of a QUALPI are stored in a normalized superposition and can be operated on simultaneously. A QUALPI can be constructed from a classical image via a preparation whose complexity is approximately linear in the image size. Some common geometric transformations, such as symmetry transformation, rotation, etc., can be performed conveniently with QUALPI. Based on these geometric transformations, a fast rotation-invariant quantum image registration algorithm is designed for log-polar images. Performance comparison with classical brute-force image registration method reveals that our quantum algorithm can achieve a quartic speedup.     

Quaternion Fourier-Mellin moments for color images

The quaternion Fourier-Mellin moments for describing color images are introduced, which can be seen as the generalization of traditional Fourier-Mellin moments for gray-level images. Then, the quaternion Fourier-Mellin moment invariants are derived, which could be a useful tool in color object recognition tasks that require the similarity invariance. In addition, the problem of color image registration using quaternion Fourier-Mellin moments is discussed. The registration method can match color images differing in rotation and scaling. The advantage of our method is that it can process color image directly, without losing color information. Experimental results validate the effectiveness of the method we proposed.     

An image rectification scheme and its applications in RST invariant digital image watermarking

This paper presents an image rectification scheme that can be used by any image watermarking algorithm to provide robustness against rotation, scaling and translation (RST) transformations. Rotation and scaling transformations in the spatial domain result in cyclically translational shifts in the log-polar mapping (LPM) of the magnitude of the Fourier transform spectrum of an image. We cut a small block from the LPM domain as a matching template. A new filtering method is proposed to compute the cross-correlation between this template and the magnitude of the LPM of the image having undergone RST transformations to detect the rotation and scaling parameters. We employ the same strategy in the spatial domain to detect the translational parameters in the spatial domain. The scheme can also be used to detect image flipping. The cost of the templates is low and the templates can even be compressed. The detection accuracy for rotation, scaling and translation is also presented. We compare the matching results for the different filters and their performance by the three criteria: signal-to-noise ratio (SNR), peak-to-correlation energy (PCE), and Horner efficiency. We demonstrate that our phase-only filtering method is the only one that can be used in the LPM domain. We also introduce three applications for this rectification scheme and give their experimental results.

Anytime similarity measures for faster alignment

Image alignment refers to finding the best transformation from a fixed reference image to a new image of a scene. This process is often optimizing a similarity measure between images, computed based on the image data. However, in time-critical applications state-of-the-art methods for computing similarity are too slow. Instead of using all the image data to compute similarity, one could use only a subset of pixels to improve the speed, but often this comes at the cost of reduced accuracy. These kinds of tradeoffs between the amount of computation and the accuracy of the result have been addressed in the field of real-time artificial intelligence as deliberation control problems. We propose that the optimization of a similarity measure is a natural application domain for deliberation control using the anytime algorithm framework. In this paper, we present anytime versions for the computation of two common image similarity measures: mean squared difference and mutual information. Off-line, we learn a performance profile specific to each measure, which is then used on-line to select the appropriate amount of pixels to process at each optimization step. When tested against existing techniques, our method achieves comparable quality and robustness with significantly less computation.