基于小波分解和互信息的图像配准方法

根据小波分解和互信息测度的原理,提出一种快速的图像配准方法。首先,对原图像进行小波分解,在保证配准精度下对分解图像进行灰度压缩,以减少配准参数的计算量,并利用最大互信息准则和下降单纯形的搜索策略找到最优配准参数实现图像配准。实验结果表明,这种图像配准方法能在保证配准精度条件下,提高配准的速度。     

基于SIFT与互信息筛选优化的遥感影像配准

针对多源多尺度影像配准中存在误匹配率较高和配准精度较低的问题,提出了一种基于（Scale-Invariant Feature Transform SIFT）与互信息筛选优化的影像配准算法。首先,采用SIFT算法进行特征点提取,通过快速最近邻逼近搜索（Fast Approximate Nearest Neighbors Search Library,FLANN）算法完成待配准影像的粗匹配,其次,在初始匹配点周围建立4×4邻域,计算匹配点之间的互信息值,对互信息值较小的匹配点进行剔除,寻求筛选优化后的最优变换矩阵,最后输出与基准影像互信息值最大的配准后影像作为最佳配准结果。实验结果表明：该方法与SIFT算法相比可以有效地剔除误匹配点并提高了配准精度。该方法可以应用于多源多尺度遥感影像配准,能够有效地提高配准精度。     

互信息在图像检索中的应用

介绍了一种用互信息来衡量相似性图像检索方法.该方法首先生成一种在统计上有代表性的视觉模式,使用这种模式的分布作为图像内容的描述符;基于该内容描述,设计了其互信息的计算方法以衡量图像的相似性.实验结果表明,在图像检索中,相对于其它如KL散度和L2规范等方法,互信息是一种更为有效的衡量相似性的方法.     

基于角点特征和最大互信息的图像配准

基于互信息的配准方法是图像配准领域的重要方法。互信息是图像配准中常用的相似性度量,具有鲁棒、精度高等优点,但基于互信息的配准计算量大,制约了它的实际应用。文章提出一种基于角点和最大互信息配准方法:首先采用间接算法来计算曲率的极大值点,从而能快速准确的提取角点集;接着计算两幅图像角点集间的互信息,最后通过POWELL算法搜索使互信息最大以实现配准。实验表明,该算法计算简单,配准速度快,具有更好的精确性和鲁棒性。     

自适应特征点检测的可见-红外图像配准

目的 针对可见—红外图像之间配准点的数量不足、分布严重不均匀以及配准点之间的错配率高这3个核心问题,提出一种基于自适应特征点检测的可见—红外图像配准方法。方法 本文提出的自适应特征点检测方法,以Harris corner作为基本特征点;以特征点数目与空间分布为检测目标,从而自动地估计合适不同空间位置的特征点的检测阈值。在特征点对匹配中,将梯度方向与互信息相融合有效地添加了相似性函数的空间位置信息。结果 自适应Harris corner检测方法能够有效地提供空间分布均匀、数量充足的特征点。而梯度方向与互信息相融合的相似性匹配函数提高特征点的匹配率20%,降低配准误差50%。结论 本文提出的多传感器图像配准方法能够快速、准确地实现可见光图像与红外图像之间的配准,在CCD-IR图像融合领域具有很好的实用价值。     

基于改进图像方向信息测度的表格图像增强算法

提出了基于图像方向信息测度的图像增强算法。算法利用图像方向信息测度,在像素级对图像像素进行分类,然后针对表格图像特性,利用改进的图像方向信息测度,对边缘点进行区域连接,使得边缘点能完全地覆盖图像中的字线和表格边缘;利用得到的边缘信息和平滑信息,对图像进行空域滤波处理,初步增强图像;利用模糊增强算法,对图像进行对比度增强,达到较好的增强效果。实验表明,该算法能有效增强对比度极低的表格图像,使得结果图像有很好的视觉效果。     

Mutual information matching in multiresolution contexts

Image registration methods based on maximization of mutual information have shown promising results for matching of 3D multimodal brain images. This paper discusses the effects of multiresolution approaches to rigid registration based on mutual information, aiming for an acceleration of the matching process while maintaining the accuracy and robustness of the method. Both standard mutual information and a normalized version are considered. The behaviour of mutual information matching in a multiresolution scheme is examined for pairs of high resolution magnetic resonance (MR) and computed tomography (CT) images and for low resolution MR images paired with either positron emission tomography (PET) images or low resolution CT images. Two methods of downscaling the images are compared: equidistant sampling and Gaussian blurring followed by equidistant sampling. The experiments show that a multiresolution approach to mutual information matching is an appropriate method for images of high (sampling) resolution, achieving an average acceleration of a factor of almost 2. For images of lower resolution the multiresolution method is not recommended. The little difference observed between matching with standard or normalized mutual information seems to indicate a preference for the normalized measure. Gaussian blurring of the images before registration does not improve the performance of the multiresolution method.     

Texture image retrieval using adaptive tetrolet transforms

This paper proposes a novel technique for texture image retrieval based on tetrolet transforms. Tetrolets provide fine texture information due to its different way of analysis. Tetrominoes are applied at each decomposition level of an image and best combination of tetrominoes is selected, which better shows the geometry of an image at each level. All three high pass components of the decomposed image at each level are used as input values for feature extraction. A feature vector is created by taking standard deviation in combination with energy at each subband. Retrieval performance in terms of accuracy is tested on group of texture images taken from benchmark databases: Brodatz and VisTex. Experimental results indicate that the proposed method achieves 78.80% retrieval accuracy on group of texture images D1 (taken from Brodatz), 84.41% on group D2 (taken from VisTex) and 77.41% on rotated texture image group D3 (rotated images from Brodatz).     

用混合弹性模型解决图象变形匹配问题

由于用传统的刚体匹配方法难以解决待匹配图象之间的结构差异 ,因此需要引入变形模型来进行图象的非刚体匹配 .为此提出了一种利用混合弹性模型 (HEM)来解决图象变形匹配问题的新方法 .该方法不需要预先提取图象的特征 ,而是直接利用匹配图象之间的灰度信息来实现图象之间的匹配 .匹配时 ,首先通过基于主轴的方法来实现两幅图象之间的全局仿射匹配 ;然后利用线性弹簧网模型来求取两幅图象之间的相关性 ,并进一步利用薄板样条模型来实现两幅图象的变形匹配 .该方法在匹配过程中还采取了多分辨率匹配策略 ,合成图象和医学脑图象的实验结果表明 ,该方法是有效的     

Unsupervised image matching based on manifold alignment

This paper challenges the issue of automatic matching between two image sets with similar intrinsic structures and different appearances, especially when there is no prior correspondence. An unsupervised manifold alignment framework is proposed to establish correspondence between data sets by a mapping function in the mutual embedding space. We introduce a local similarity metric based on parameterized distance curves to represent the connection of one point with the rest of the manifold. A small set of valid feature pairs can be found without manual interactions by matching the distance curve of one manifold with the curve cluster of the other manifold. To avoid potential confusions in image matching, we propose an extended affine transformation to solve the nonrigid alignment in the embedding space. The comparatively tight alignments and the structure preservation can be obtained simultaneously. The point pairs with the minimum distance after alignment are viewed as the matchings. We apply manifold alignment to image set matching problems. The correspondence between image sets of different poses, illuminations, and identities can be established effectively by our approach.     

基于动态融合蚁群遗传算法的医学图像配准

将基于动态融合的蚁群遗传算法作为一种新的图像配准优化算法应用在多模医学图像配准中。该算法以互信息作为相似性测度,生成初始信息素分布,采用蚁群算法搜索最优变换参数,其中动态融合策略提高了混合算法的搜索效率。仿真实验结果表明,该算法有效地避免信息函数的局部极值,减少大量重复运算,提高了配准的效率,配准结果具有良好的稳定性。     

基于互信息与边缘互距离的医学图像配准

提出了一种基于互信息与边缘互距离信息的医学图像配准新测度。该种测度既利用了待配准图像间的灰度互信息,又利用了图像边缘间的互距离均值和互距离方差空间信息,从而改进了互信息测度。实验证明这种测度得到的配准参数曲线光滑且峰值尖锐,收敛范围宽,对图像大小有更强的鲁棒性,在图像互信息值一样的情况下,仍有辨识能力。     

一种结合互信息和模板匹配的配准方法

传统的互信息法直接对大图进行互信息配准,容易出现局部极值并产生误匹配现象,针对该问题,提出一种将互信息与模板匹配相结合的配准方法,采用互信息为相似度准则进行图像的模板匹配,得到候选的匹配子图,通过待配准大图其余部分与模板和子图的空间关系,得到配准后的大图,分别计算按照各个候选子图配准情况下的待配准图的互信息,得到最大的互信息所对应的子图,确定最终的配准结果。实验结果表明,该方法在对灰度差较大的图像进行配准时,能够获得良好的效果。     

Conflating two polygonal lines

A basic technique in comparing and detecting changes in geographical spatial data from satellite images consists of identifying linear features or edges in the image and then matching those features. A chain of connected linear features which form a polygonal line is used as the basic unit for matching two images. We develop a distance measure between two polygonal lines and an efficient algorithm for conflating or optimally matching two polygonal lines based on this distance measure. We show that some of the alternative approaches used in the literature, including Hausdorff's distance, fail to satisfy the basic requirements of a distance measure for image conflation.     

基于神经网络的边缘强度互相关匹配可信度分析

匹配可信度是分析图象匹配质量的主要指标,针对归一化边缘强度互相关匹配算法,研究了基于神经网络的匹配可信度判别方法,即以参考图与若干实时图的匹配实验结果作为训练用样本数据,然后利用ＢＰ网络进行训练,再将训练后的网络用于匹配可信度的判别。通过实际卫片与航片图象对的匹配实验,证实了该方法的有效性。     

基于边缘特征点互信息熵的医学图像配准方法

基于互信息熵的图像配准方法已经被广泛应用于医学图像配准中,为克服互信息配准方法的不足,结合图像空间结构信息,本文提出一种基于边缘特征点互信息熵的医学图像配准方法,设计了包括互信息熵、图像空间结构和形状特征点等多信息融合的配准新测度。算法首先采用改进的形态学梯度提取医学图像边缘轮廓;然后构造了以边缘区域特征和梯度信息为基础的特征点互信息能量函数,并通过最小化能量函数来获取配准参数;最后,结合梯度下降法优化策略,实现图像配准。实验研究表明,该方法在保证了配准精度的同时,配准速度较快、鲁棒性较好、综合性能优良,具有一定的临床推广价值。     

基于序列影像的城市三维建模

提出了一种从序列图像建立城市三维模型的算法框架。该方法使用三条序列影像和匹配生成的数字表面模型，结合几何约束的多视特征匹配，从图像获取三维点、线信息。同时讨论了基于感知编组的几何模型生成算法，以及纹理优选与自动映射。实验结果证明该方法是有效的。     

结合形态学梯度互信息和多分辨率寻优的图像配准新方法

对互信息配准法进行算法改进. 在互信息基础上结合形态学梯度作为新的图像配准测度, 不仅考虑所有体素信息, 而且有效结合像素在空间位置的相互关系. 将粒子群优化 (Particle swarm optimization, PSO) 算法这种全局寻优算法和 Powell 这一局部寻优算法相结合, 前者的配准结果为后者的算法优化提供了非常有效的初始点, 优化时间大为减少. 借鉴小波变换中多分辨率的思想, 在低分辨率图像中粗略配准后, 上升到高分辨率图像上进一步细化配准结果, 增加算法鲁棒性. 实验结果证明, 本文算法效果良好, 寻优过程在数分钟内完成, 能够满足诊断和科研的实时性要求.     

Unsupervised measures for parameter selection of binarization algorithms

In this paper, we propose a mechanism for systematic comparison of the efficacy of unsupervised evaluation methods for parameter selection of binarization algorithms in optical character recognition (OCR). We also analyze these measures statistically and ascertain whether a measure is suitable or not to assess a binarization method. The comparison process is streamlined in several steps. Given an unsupervised measure and a binarization algorithm we: (i) find the best parameter combination for the algorithm in terms of the measure, (ii) use the best binarization of an image on an OCR, and (iii) evaluate the accuracy of the characters detected. We also propose a new unsupervised measure and a statistical test to compare measures based on an intuitive triad of possible results: better, worse or comparable performance. The comparison method and statistical tests can be easily generalized for new measures, binarization algorithms and even other accuracy-driven tasks in image processing. Finally, we perform an extensive comparison of several well known measures, binarization algorithms and OCRs, and use it to show the strengths of the WV measure.