Efficient template matching for multi-channel images

Template matching is a computationally intensive problem aimed at locating a template within a image. When dealing with images having more than one channel, the computational burden becomes even more dramatic. For this reason, in this paper we investigate on a methodology to speed-up template matching on multi-channel images without deteriorating the outcome of the search. In particular, we propose a fast, exhaustive technique based on the Zero-mean Normalized Cross-Correlation (ZNCC) inspired from previous work related to grayscale images. Experimental testing performed over thousands of template matching instances demonstrates the efficiency of our proposal.     

一种快速的具有旋转不变性的模板匹配方法

传统的基于相关的匹配方法计算量相当大,而且当模板相对于搜索图有角度旋转时,匹配的计算量更大。用圆警影和zemike矩的方法对快速的且具有旋转不变性的模板匹配方法进行了研究,圆投影将图像由二维变换成一维,这样就降低了计算复杂度,通过相似性度量可进行快速地粗匹配,然后在可能的匹配点中,再用Zemike矩实现精匹配。     

A robust hit-or-miss transform for template matching applied to very noisy astronomical images

The morphological hit-or-miss transform (HMT) is a powerful tool for digital image analysis. Its recent extensions to grey level images have proven its ability to solve various template matching problems. In this paper we explore the capacity of various existing approaches to work in very noisy environments and discuss the generic methods used to improve their robustness to noise. We also propose a new formulation for a fuzzy morphological HMT which has been especially designed to deal with very noisy images. Our approach is validated through a pattern matching problem in astronomical images that consists of detecting very faint objects: low surface brightness galaxies. Despite their influence on the galactic evolution model, these objects remain mostly misunderstood by the astronomers. Due to their low signal to noise ratio, there is no automatic and reliable detection method yet. In this paper we introduce such a method based on the proposed hit-or-miss operator. The complete process is described starting from the building of a set of patterns until the reconstruction of a suitable map of detected objects. Implementation, running cost and optimisations are discussed. Outcomes have been examined by astronomers and compared to previous works. We have observed promising results in this difficult context for which mathematical morphology provides an original solution.     

图像数字水印中的模板匹配检测方法

基于对数极坐标映射(LPM),设计了图像数字水印技术中的模板匹配检测方法.该模板匹配检测方法的特点是:在图像数字水印的嵌入过程中,从对数极坐标域中切取一块模板匹配信息;计算该模板匹配信息与加入水印后的图像在LPM域中的相位谱的相关系数来进行水印的检测工作.这种方法不使用原始图像的全部信息,减少了相关性检验的计算时间,提高了工作效率.实验结果表明该方法对图像中数字水印具有较强的检测能力.     

Fast template matching algorithm for contour images based on its chain coded description applied for human face identification

A new matching algorithm for contour images described by chain coded expression is presented. In our face authentication system, the isodensity contours has been introduced to differentiate between the facial features. These isodensity contours can be transformed into chain codes. By using these coded isodensity contours, remarkable improvement in the processing performance can be expected in terms of the processing time and memory requirements.From the computer simulation performed using images of 50 people, it turned out clear that the processing time was decreased to approximately one-seventh compared to the conventional method. With respect to memory requirement, it was reduced to a quarter.     

A novel two stage template matching method for rotation and illumination invariance

Template matching is the process of determining the presence and the location of a reference image or an object inside a scene image under analysis by a spatial cross-correlation process. Conventional cross-correlation type algorithms are computationally expensive. Furthermore, when the object in the image is rotated, the conventional algorithms cannot be used for practical purposes.In this paper, an algorithm for a rotation invariant template matching method based on the combination of the projection method and Zernike moments is proposed. The algorithm consists of two stages. In the first stage, the matching candidates are selected using a computationally low cost feature. Frequency domain calculation was adopted to reduce the computational cost for this stage. In the second stage, rotation invariant template matching is performed only on the matching candidates using Zernike moments.     

A new residual fusion classification method for hyperspectral images

In this paper, we propose a novel residual fusion classification method for hyperspectral image using spatial–spectral information, abbreviated as RFC-SS. The RFC-SS method first uses the Gabor texture features and the non-parametric weighted spectral features to describe the hyperspectral image from both aspects of spatial and spectral information. Then it applies the residual fusion method to save the useful information from different classification methods, which can greatly improve the classification performance. Finally, the test sample is assigned to the class that has the minimal fused residuals. The RFC-SS classification method is tested on two classical hyperspectral images (i.e. Indian Pines, Pavia University). The theoretical analysis and experimental results demonstrate that the RFC-SS classification method can achieve a better performance in terms of overall accuracy, average accuracy, and the Kappa coefficient when compared to the other classification methods.     

基于参数模板匹配的指纹中心定位

目前广泛采用最大曲率方法实现指纹中心定位,但在指纹中心缺失情况下,该方法定位并不准确.为解决该问题,提出了一种基于相关参数模板匹配的指纹中心定位新方法,对FVC2004指纹数据库的测试表明,在指纹经过初步增强的基础上,借助最小距离判别,新方法能够对指纹中心准确定位,与最大曲率方法相比,在中心点缺失的情况下,有更高的鲁棒性.     

A program for template matching of protein sequences

The matching of a template to a protein sequence is simplified by treating it as a special case of sequence alignment. Restriction of the distances between motifs in the template controls against spurious matches within very long sequences. The program using this algorithm is fast enough to be used in scanning large databases for sequences matching a complex template.     

改进的归一互相关法的灰度图像模板匹配方法

在灰度图像的模板匹配方法中,归一化互相关（NC）法具有很高的准确性和适应性,以及抗干扰能力强,受噪声的影响小和不存在伪匹配等特点,同时对图像灰度值的线性变换具有“免疫性”。但NC方法计算时间长、计算数据量相对较大,所以实时性较差。提出通过改进搜索方法以提高归一化互相关（NC）法的运算速度。实验仿真结果证明了改进的归一化互相关（NC）法比传统的NC方法具有精度高、速度快的特点,有一定的适用价值。     

基于模板匹配法的散焦图像模糊度估计

提出一种新的散焦图像模糊度估计方法。由图像中的阶跃型边缘提取线扩展函数。根据匹配滤波可实现最优信噪比信号检测的思想,引入模板匹配的方法计算线扩展函数的标准差。由点扩展函数的圆周对称性从线扩展函数的标准差得到高斯型点扩展函数的标准差。实验结果表明,该方法能够准确地估计散焦模糊图像点扩展函数的标准差。将点扩展函数的标准差作为一种评价图像模糊度的测度。实验表明该测度符合人眼的视觉特性,可以很好地判定散焦模糊图像的模糊度。     

Feature mining and pattern classification for steganalysis of LSB matching steganography in grayscale images

In this paper, we present a scheme based on feature mining and pattern classification to detect LSB matching steganography in grayscale images, which is a very challenging problem in steganalysis. Five types of features are proposed. In comparison with other well-known feature sets, the set of proposed features performs the best. We compare different learning classifiers and deal with the issue of feature selection that is rarely mentioned in steganalysis. In our experiments, the combination of a dynamic evolving neural fuzzy inference system (DENFIS) with a feature selection of support vector machine recursive feature elimination (SVMRFE) achieves the best detection performance. Results also show that image complexity is an important reference to evaluation of steganalysis performance.     

多方向模板的构造及匹配的快速算法

多方向模板匹配是图像处理的重要方法。对原有的Prewitt模板进行了扩充，构造了新的多方向模板，建立了相应的快速模板匹配算法。在图像处理中，通过多方向模板，图像得到了加强，并且提取了图像相关的线性特征，实行快速算法在理论上比经典算法快4．57倍，节省了运行时间。     

A generalization of template matching for recognition of real objects

Pattern-recognition methods are discussed under the general headings of feature extraction and template matching. Problems arising from these methods are of two types: those arising in the process of recognition and those arising when making measurements on objects after recognition. It is indicated how template matching can avoid these latter types of problems and how a generalization of the normal notion of template can surmount problems of the first type. Such a generalized template matching procedure for recognizing cells in a microscope slide is discussed. The generalized template, in this case, makes use of the fact that the cells seem “bubble-like”. It expands a “bubble” template and detects matches between this and the visual data.     

一种基于ORB的快速大视角图像匹配算法

针对ASIFT算法抗大视角变换能力较好,但运算效率低的缺点,提出一种基于ORB的快速大视角图像匹配算法.该算法结合透视变换模型和ORB算法对ASIFT中的仿射变换模型和SIFT算法进行优化,在粗匹配算法获得单应性矩阵的基础上进行精匹配,有效减少了模拟次数,并提高了算法运算效率.实验结果表明,所提出算法具备抗视角变换能力,计算速度比ASIFT算法提高10倍,实时性强,工程使用价值高.     

一种基于SIFT特征的铁道检测图片的匹配方法

提出一种基于SIFT特征的铁道检测图片的匹配方法。由人工标定铁路上的目标位置图片,通过匹配算法计算匹配图片与目标位置图片可匹配SIFT特征点的数量,利用DTW最优路径规划得到全局最优的匹配结果,从匹配结果中得到一张匹配度最高的图片并将其输出用于道路检测。实验证明,该算法在效率和准确度上均有较好的表现。     

一种快速模板匹配的波形识别算法

本文以数字波形为例,提出波形幅值向量化处理快速建立匹配模板.把输入信号的波形做准归一化处理与模板进行匹配,求出与所有模板的距离.实验结果表明此方法不仅可行,而且正确率和执行效率相当高.     

高光谱图像小样本分类的卷积神经网络方法

目的 与传统分类方法相比,基于深度学习的高光谱图像分类方法能够提取出高光谱图像更深层次的特征。针对现有深度学习的分类方法网络结构简单、特征提取不够充分的问题,提出一种堆叠像元空间变换信息的数据扩充方法,用于解决训练样本不足的问题,并提出一种基于不同尺度的双通道3维卷积神经网络的高光谱图像分类模型,来提取高光谱图像的本质空谱特征。方法 通过对高光谱图像的每一像元及其邻域像元进行旋转、行列变换等操作,丰富中心像元的潜在空间信息,达到数据集扩充的作用。将扩充之后的像素块输入到不同尺度的双通道3维卷积神经网络学习训练集的深层特征,实现更高精度的分类。结果 5次重复实验后取平均的结果表明,在随机选取了10%训练样本并通过8倍数据扩充的情况下,Indian Pines数据集实现了98.34%的总体分类精度,Pavia University数据集总体分类精度达到99.63%,同时对比了不同算法的运行时间,在保证分类精度的前提下,本文算法的运行时间短于对比算法,保证了分类模型的稳定性、高效性。结论 本文提出的基于双通道卷积神经网络的高光谱图像分类模型,既解决了训练样本不足的问题,又综合了高光谱图像的光谱特征和空间特征,提高了高光谱图像的分类精度。