基于图分割与Hausdorff距离的多分辨率影像匹配

针对具有复杂场景的航拍图像提出了一种基于图分割理论与Hausdorff距离的多分辨率影像匹配方法。在高斯金字塔图像模型中,低分辨率的图像通过图分割方法,充分考虑图像中的局部和全局的信息,提取到稳定和完整的图像区域边界,并以区域边界作为待匹配的曲线。再通过计算曲线的统计特性作为图像间待匹配特征,并由信号相关的度量方法粗估计出图像间全局仿射变换参数。利用粗估计的参数在高分辨率层次上进一步通过基于Hausdorff距离的匹配方法搜索到精确的变换参数。实验结果表明,该方法在较大变形和强噪音干扰的情况下对复杂场景的图像也能有效地完成匹配。     

Shape matching using LAT and its application to handwritten numeralrecognition

This paper describes an iterative technique for gradually deforming a mask binary image with successive local affine transformation (LAT) operations so as to yield the best match to an input binary image as one new and promising approach toward robust handwritten character recognition. The method uses local shapes in the sense that the LAT of each point at one location is optimized using locations of other points by means of least-squares data fitting using Gaussian window functions. It also uses a multiscale refinement technique that decreases the spread of window functions with each iteration. Especially in handwritten character recognition, structural information is indispensable for robust shape matching or discrimination. The method is enhanced to explicitly incorporate structures by weighting the above least-squares criterion with similarity measures of both topological and geometric features of the mask and input images. Moreover, deformation constraints are imposed on each iteration, not only to promote and stabilize matching convergence but also to suppress an excessive matching process. Shape matching experiments have been successfully carried out using skeletons of totally unconstrained handwritten numerals     

模糊形态联想记忆网络抗随机噪声研究及应用

利用灰度图像分解的思想,结合模糊形态联想记忆网络的方法,提高了模糊形态联想记忆网络对随机噪声的抗噪能力。成功地解决了灰度图像在含有随机噪声时的模糊联想记忆问题,并把该方法推广到对彩色图像的处理,从而给出了一种较好地恢复含噪灰度图像和彩色图像的途径。通过实验,验证了该方法的良好性能,取得了较理想的结果。     

k-NN classification of handwritten characters via accelerated GAT correlation

This paper addresses the problem of reinforcing the ability of the k-NN classification of handwritten characters via distortion-tolerant template matching techniques with a limited quantity of data. We compare three kinds of matching techniques: the conventional simple correlation, the tangent distance, and the global affine transformation (GAT) correlation. Although the k-NN classification method is straightforward and powerful, it consumes a lot of time. Therefore, to reduce the computational cost of matching in k-NN classification, we propose accelerating the GAT correlation method by reformulating its computational model and adopting efficient lookup tables. Recognition experiments performed on the IPTP CDROM1B handwritten numerical database show that the matching techniques of the simple correlation, the tangent distance, and the accelerated GAT correlation achieved recognition rates of 97.07%, 97.50%, and 98.70%, respectively. The computation time ratios of the tangent distance and the accelerated GAT correlation to the simple correlation are 26.3 and 36.5 to 1.0, respectively.     

改进的基于凸壳仿射不变量的图像识别和配准算法

提出了一种新的基于凸壳和仿射不变量的图像识别和配准的方法。该方法利用从参考图和测试图中得到的特征点提取其凸壳,并计算凸壳的仿射不变特征向量。通过比较参考图和测试图特征向量的一致性,建立它们的仿射变换关系,最后利用凸壳内特征点的匹配来实现识别和配准。该方法的优点是即使目标物体被部分覆盖或者缺损,其图像也能够达到较好的识别和配准效果。     

动态核的形态分解联想算法及抗随机噪声研究

在灰度图像分解算法和动态核形态联想记忆网络的基础上,提出了一种新的联想记忆算法--动态核的形态分解联想算法.该方法显著地提高了联想记忆抗随机噪声的能力,较好地解决了灰度图像在含噪时的联想记忆和识别的问题,从而给出了一种恢复含噪灰度图像的途径,并把该方法推广到了彩色图像的处理.通过实验,验证了该方法的良好性能,取得了理想的结果.     

匹配与姿态估计的粒子群优化算法

点模式匹配问题是机器视觉与模式识别领域中一个基础问题,在目标识别、医学图像配准、遥感图像匹配、姿态估计等方面都得到广泛应用。提出一种在仿射变换下利用粒子群优化算法进行图像点模式下的匹配与姿态估计的方法。算法首先把点集匹配问题转化为解空间为仿射参数空间下的目标函数优化问题,然后运用粒子群算法对相应的变换参数进行搜索,获得问题最优解。本文贡献如下：1)给出一种仿射参数的初始估计方法,提高了后续算法搜索效率;2)引入阈值和次近点规则,改进了最近点匹配搜索方法,能较好地拒绝出格点（outliers）,并提高算法有效性;3)从两方面对PSO方法进行了改进,加强了原PSO的全局和局部搜索能力。实验结果表明,算法具有有效性和鲁棒性。     

几何特征形态学车牌识别系统研究

优化识别车牌识别问题,由于图像中的环境背景受到天气、照明等因素的影响,车牌定位不清。为解决上述问题,提出了一种结合车牌图像自身几何特征的数学形态学车牌识别系统的方法。首先应用直方图的灰度增强和局部阈值算法对车牌图像进行的预处理,通过对比采用梯度算子Roberts对图像进行有效的边沿检测,再根据提出的几何特征形态学车牌定位识别方法对灰度车牌图像进行车牌区域精确定位,采用模板匹配和神经网络方法实现字符识别。通过对实际场景中车牌图像样本进行仿真,证明了上述方法的有效性,且借助于实时性好的LabVIEW平台,较好地实现车牌识别定位优化问题,为实际交通管理提供了依据。     

基于小波和DCT的灰度压印字符图像的特征抽取

标牌压印字符是“反光差”的凹凸字符,通常的基于二值化图像的字符特征抽取方法都不适宜。提出了基于灰度图像的标牌压印字符特征抽取新方法,首先对灰度字符进行圆周投影,然后利用小波变换,将投影曲线分解为大致信号和细节信号的子样本,最后对子样本进行DCT变换,生成凹凸字符的特征矢量。该方法是直接对灰度图像抽取字符特征,不仅可以尽量多地保持原始字符的特征,而且克服了传统的字符图像特征抽取时,过分依赖于二值化算法、抗干扰性差等弊病。对标牌压印有限凹凸字符集进行特征抽取和识别实验表明,该特征抽取方法具有尺度和旋转不变性,有较好的抗干扰性和很好的分类性能,实用价值很高。     

基于曲率的全仿射曲线图像配准

为了解决视点变化造成曲线图像匹配和识别困难的问题,利用全仿射模型建立仿射变换图库,在图库样本中通过曲率信息找出与目标样本相关性最优的样本,从而达到曲线图像匹配和识别的目的。基于曲率的全仿射曲线图像匹配的方法, 首先根据全仿射模型对样本图像建立样本库,对样本库中每一个样本通过等间隔偏移进行采样,然后对采样后的子样本建立曲率样本子集,将目标图像与样本子集中的每一个样本进行快速最近邻搜索算法相似性匹配;最后通过匹配结果,找到最优匹配样本。实验结果表明,基于曲率的全仿射曲线图像配准具有较高的成功率,并且与传统算法相比具有更多的优点。     

A Bayesian framework for deformable pattern recognition withapplication to handwritten character recognition

Deformable models have recently been proposed for many pattern recognition applications due to their ability to handle large shape variations. These proposed approaches represent patterns or shapes as deformable models, which deform themselves to match with the input image, and subsequently feed the extracted information into a classifier. The three components-modeling, matching, and classification-are often treated as independent tasks. In this paper, we study how to integrate deformable models into a Bayesian framework as a unified approach for modeling, matching, and classifying shapes. Handwritten character recognition serves as a testbed for evaluating the approach. With the use of our system, recognition is invariant to affine transformation as well as other handwriting variations. In addition, no preprocessing or manual setting of hyperparameters (e.g., regularization parameter and character width) is required. Besides, issues on the incorporation of constraints on model flexibility, detection of subparts, and speed-up are investigated. Using a model set with only 23 prototypes without any discriminative training, we can achieve an accuracy of 94.7 percent with no rejection on a subset (11,791 images by 100 writers) of handwritten digits from the NIST SD-1 dataset     

基于仿射迭代模型的特征点匹配算法

图像序列中的特征点匹配是计算机视觉中的一个基本问题,也是目标识别、图像检索以及3维重建等问题的基础。为了提高图像匹配的精度,提出了一种针对两幅图像的高精度特征点自动匹配算法。该算法首先分析并提出两幅图像中相应特征点的邻域窗口之间的单应映射可以用仿射变换模型来近似;然后通过快速的基于仿射变换模型的迭代优化方法,不仅估计并矫正了相应邻域窗口之间的透视畸变,同时还补偿了在特征点检测阶段对相应特征点的定位误差,从而使匹配结果达到子像素级精度;最后通过真实图像的实验以及与现有算法的比较结果表明,该算法不仅得到了更多的匹配关系,还提高了特征点匹配的精度。     

New image matching technique based on hyper-vectorisation of grey level sliced binary image

Image matching is an important area of research in the field of artificial intelligence, machine vision and visual navigation. A new image matching scheme in which grey scale images are quantised to form sub-band binary images is presented. The information in the binary images is then signaturised and the signatures are sorted as per significance. These sorted signatures are then normalised to transform the represented image pictorial features in the form of a hyper-dimensional vector cluster. For the image matching, the two clusters from both the images are compared in the transformed domain. This comparison yields efficient results directly in the image spatial domain avoiding the need of image inverse transformation for the interpretation of results. As compared with the conventional techniques, this comparison avoids the wide range of square error calculations all over the image. It also directly guides the solution in an iterative fashion to converge towards the true match point. The process of signaturisation is based on image local features and is moulded in a way to support the scale and rotation-invariant template matching as well. A four-dimensional solution population scheme has also been presented with an associated matching confidence factor. This factor helps in terminating the iterations when the essential matching conditions have been achieved. The proposed scheme gives robust and fast results for normal, scaled and rotated templates. Speed comparison with older techniques shows the computational viability of this new technique and its much lesser dependence on image size. The method also shows noise immunity at 30 dB additive white Gaussian noise and impulsive noise.     

Document image binarization based on texture features

Binarization has been difficult for document images with poor contrast, strong noise, complex patterns, and/or variable modalities in gray-scale histograms. We developed a texture feature based thresholding algorithm to address this problem. Our algorithm consists of three steps: 1) candidate thresholds are produced through iterative use of Otsu's algorithm (1978); 2) texture features associated with each candidate threshold are extracted from the run-length histogram of the accordingly binarized image; 3) the optimal threshold is selected so that desirable document texture features are preserved. Experiments with 9,000 machine printed address blocks from an unconstrained US mail stream demonstrated that over 99.6 percent of the images were successfully binarized by the new thresholding method, appreciably better than those obtained by typical existing thresholding techniques. Also, a system run with 500 troublesome mail address blocks showed that an 8.1 percent higher character recognition rate was achieved with our algorithm as compared with Otsu's algorithm     

基于SIFT图像配准的维吾尔语文字识别方法

提出一种新的维吾尔语文字识别研究方法。首先,建立字符样本库,并对库中文字图像归一化。然后,将测试图像与样本图像进行垂直和水平双方向投影相关性检测,对与测试图像双投影相关性较高的样本字符进行笔画数特征提取,得到预分类结果。最后,将测试图像与预分类结果进行SIFT关键点检测、方向描述子生成与配准,与测试图片匹配点对最多的预分类结果为识别结果,并输出该结果标记符号对应的维吾尔语字符。实验结果表明:该方法能减少字符样本的数量,并有效解决测试图像尺度与几何形变的差异造成的匹配困难问题。     

Solution of the Simultaneous Pose and Correspondence Problem Using Gaussian Error Model

The use of hypothesis verification is recurrent in the model-based recognition literature. Verification consists in measuring how many model features transformed by a pose coincide with some image features. When data involved in the computation of the pose are noisy, the pose is inaccurate and difficult to verify, especially when the objects are partially occluded. To address this problem, the noise in image features is modeled by a Gaussian distribution. A probabilistic framework allows the evaluation of the probability of a matching, knowing that the pose belongs to a rectangular volume of the pose space. It involves quadratic programming, if the transformation is affine. This matching probability is used in an algorithm computing the best pose. It consists in a recursive multiresolution exploration of the pose space, discarding outliers in the match data while the search is progressing. Numerous experimental results are described. They consist of 2D and 3D recognition experiments using the proposed algorithm.     

一种SAS图像拼接改进算法

SWF算子提取的特征点对图像尺度和旋转具有不变性,对环境变化、噪声以及仿射变换都具有很强的鲁棒性。本文在基于SIFT算子和RANSAC算法的图像拼接算法的基础上,根据SAS图像分辨率高、相邻帧图像重合度大且只存在平移和旋转变换的特点,对提取稳定特征点和计算拼接图像之间特征点匹配对的过程进行了改进。实际的SAS湖试图像拼接处理结果说明,改进算法提高了拼接算法的速度和图像之间的配准精度,增强了算法的鲁棒性。     

一种鲁棒的序列图像自动拼接方法

针对传统图像拼接方法中鲁棒性差、计算量大及自动化程度低等问题,提出一种鲁棒性高的序列图像自动拼接方法。该方法首先采用Harris角点检测算子对经Wallis滤波后的序列图像进行特征点提取,并结合Forstner算子对特征点进行精确定位。然后基于所提取的特征点,采用邻域灰度互相关法进行序列图像的特征点匹配,得到粗匹配点集,并运用RANSAC算法对粗匹配点集处理得到精匹配点集,由精匹配点集求出较高精度的基础矩阵及极线,并由极线约束引导匹配得到高精度的匹配点对,再运用双向松弛整体匹配算法进一步剔除少数位于极线上的误匹配点。最后利用所得的高精度匹配点对,求解序列图像间的仿射变换关系,并进行图像的坐标变换和融合,从而实现序列图像的自动拼接。实验结果表明,该方法拼接效果理想,鲁棒性高,整个拼接过程全自动,不需要人工干预,具有较高的实用价值。     

基于分类方法的分形图像压缩

分形图像因其图像块的自相似性可以得到较高的压缩比、有效的减少冗余.在分形和局部迭代函数系统理论的基础上,介绍了一种灰度排列法的编码方案.该方法按照1/4子快的灰度均值,通过放射变换将图像块分为3大主类.每个子块在相应的父块类中去寻找最佳匹配的父块,从而使无效父块在搜索过程中的比较次数明显减少.实验结果表明,该算法能有效的压缩图像,压缩比高、解码图像质量好、压缩时间较短.