Optimal template matching by nonorthogonal image expansion using restoration

In this paper we present a novel approach for template matching. The basic principle is expansion matching and it entails signal expansion into a set of nonorthogonal templatesimilar basis functions. The coefficients of this expansion signify the presence of the template in the corresponding locations in the image. We demonstrate that this matching technique is robust in conditions of noise, superposition, and severe occlusion. A new and more practical discriminative signal-to-noise ratio (DSNR) for matching is proposed that considers even the filter's off-center response to the template as “noise”. We show that expansion yields the optimal linear operator that maximizes the DSNR and results in a sharp response to the matched template. Theoretical and experimental comparisons of expansion matching and the widely used correlation matching demonstrate the superiority of our approach. Correlation matching (also known as matched filtering) yields broad peaks and spurious responses, both of which hamper good detection. We also show that the special case of expansion with a dense set of self-similar basis functions is equivalent to signal restoration. Expansion matching can be implemented by restoration techniques and also by our recently developed lattice architecture.     

New scale invariant template matching technique using hyper space image representation

A new approach for the template image matching is being presented. The method first converts the image into edges, then, the vital information of these edges has been presented as a set of vectors in a four dimensional hyper-space. A modified Radon Transform has been proposed to facilitate this vectorization process. All the above processing is being done offline for the main image of the area. The template image has also been vectorized in a same fashion in real time which is to be matched with the main image. A vector matching algorithm has been proposed to deliver match location with a very low computational cost. It works for a wide range of template scaling and noise conditions which were not there in the previous algorithms found in the literature.     

模板匹配技术在图像识别中的应用

在图像目标识别技术的研究应用中,模板匹配技术是其中一个重要的研究方向,它具有算法简单、计算量小以及识别率高的特点。介绍了几种改进的模板匹配技术在图像处理、模式识别等领域的应用,包括有条码识别、生物特征识别技术(人脸识别、指纹识别等)、车牌识别、字符识别、飞机识别等。     

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 using bounded partial correlation

This paper describes a novel, fast template-matching technique, referred to as bounded partial correlation (BPC), based on the normalised cross-correlation (NCC) function. The technique consists in checking at each search position a suitable elimination condition relying on the evaluation of an upper-bound for the NCC function. The check allows for rapidly skipping the positions that cannot provide a better degree of match with respect to the current best-matching one. The upper-bounding function incorporates partial information from the actual cross-correlation function and can be calculated very efficiently using a recursive scheme. We show also a simple improvement to the basic BPC formulation that provides additional computational benefits and renders the technique more robust with respect to the parameters choice. Received: 2 November 2000 / Accepted: 25 July 2001 Correspondence to: L. Di Stefano     

基于比值法和模板匹配法的灰度图像拼接

提出一种改进的基于比值法和模板匹配法的灰度图像拼接算法。对两幅具有重叠区域的灰度图像，先通过设置拼接参数对话框，在另一幅图像中找到最佳匹配点，最后利用平滑因子对两幅图像的重叠区域进行数据融合操作，实现了灰度图像的快速自动拼接。实验证明．该方法对大部分灰度图像具有很好的拼接效果。     

Efficient parallel algorithms for image template matching onhypercube SIMD machines

Efficient parallel algorithms developed on hypercube SIMD (single-instruction multiple data-stream) machines for image template matching are presented. Most of these parallel algorithms are asymptotically optimal in their time complexities. These results improve the known bounds in the literature     

Scale-invariant template matching using histogram of dominant gradients

This paper presents a histogram-based template matching method that copes with the large scale difference between target and template images. Most of the previous template matching methods are sensitive to the scale difference between target and template images because the features extracted from the images are changed according to the scale of the images. To overcome this limitation, we introduce the concept of dominant gradients and describe an image as the feature that is tolerant to scale changes. To this end, we first extract the dominant gradients of a template image and represent the template image as the grids of histograms of the dominant gradients. Then, the arbitrary regions of a target image with various locations and scales are matched with the template image via histogram matching. Experimental results show that the proposed method is more robust to scale difference than previous template matching techniques.     

基于图像中心矩的快速模板匹配算法

提出了一种以中心矩作为匹配特征,基于积分图像的思想,并采用粗、精阶段相结合的相关跟踪算法。实验结果表明,与以往的以象素作为图像特征的相关跟踪算法不同,本算法运算速度快、实时性强、匹配精度高。采用Visual C 编程,可在40ms,即一个电视周期内完成对图像的处理。本算法在运动目标跟踪、模式识别及机器视觉等领域有着广泛的应用前景。     

Tongue image matching using color content

This paper addresses the problem of color image matching in medical diagnosis. The color matching of tongue images in different color spaces with different metrics have been investigated and is reported in this article. Two new metrics namely, sorted metric and probabilistic combined metric, are proposed. Existing distance measurements in coordinate space do not satisfy the reflexivity axiom. That means, they are not the valid metrics. To overcome this limitation, the sorted metric in coordinate space is proposed in coordinate space. To improve the matching performance, a probabilistic combined metric is proposed based on the theory of combining classifier. These metrics are applied for the matching of tongue color images and the results are encouraging.     

Efficient image matching using weighted voting

Spectral decomposition subject to pairwise geometric constraints is one of the most successful image matching (correspondence establishment) methods which is widely used in image retrieval, recognition, registration, and stitching. When the number of candidate correspondences is large, the eigen-decomposition of the affinity matrix is time consuming and therefore is not suitable for real-time computer vision. To overcome the drawback, in this letter we propose to treat each candidate correspondence not only as a candidate but also as a voter. As a voter, it gives voting scores to other candidate correspondences. Based on the voting scores, the optimal correspondences are computed by simple addition and ranking operations. Experimental results on real-data demonstrate that the proposed method is more than one hundred times faster than the classical spectral method while does not decrease the matching accuracy.     

Privacy aware group based recommender system in multimedia services

Multimedia Tools and Applications - Recommending similar-interest users’ groups in multimedia services is the problem of detecting for each registered user his/her membership to one...     

基于模板匹配与运动预测的跟踪算法研究

本文所介绍的跟踪算法使用了基于MCD距离的相似性度量方法进行模板匹配,并采用了一种模板自适应更新策略,使得跟踪算法对环境的适应能力和稳定性得到了较大的提高.此外,还根据目标在运动过程中具有轨迹连续性的特点,加入了预测算法的使用.实验结果表明,该算法能较好地解决跟踪过程中目标受干扰和被遮挡等问题.     

Boundary image matching supporting partial denoising using time-series matching techniques

Multimedia Tools and Applications - In this paper, we deal with the problem of boundary image matching which finds similar boundary images regardless of partial noise exploiting time-series...     

Unconstrained automatic image matching using multiresolutionalcritical-point filters

This paper proposes a novel method for matching images. The results can be used for a variety of applications: fully automatic morphing, object recognition, stereo photogrammetry, and volume rendering. Optimal mappings between the given images are computed automatically using multiresolutional nonlinear filters that extract the critical points of the images of each resolution. Parameters are set completely automatically by dynamical computation analogous to human visual systems. No prior knowledge about the objects is necessary The matching results can be used to generate intermediate views when given two different views of objects. When used for morphing, our method automatically transforms the given images. There is no need for manually specifying the correspondence between the two images. When used for volume rendering, our method reconstructs the intermediate images between cross-sections accurately, even when the distance between them is long and the cross-sections vary widely in shape. A large number of experiments has been carried out to show the usefulness and capability of our method     

Artificial landmark-based underwater localization for AUVs using weighted template matching

This paper deals with vision-based localization techniques in structured underwater environments. For underwater robots, accurate localization is necessary to perform complex missions successfully, but few sensors are available for accurate localization in the underwater environment. Among the available sensors, cameras are very useful for performing short-range tasks despite harsh underwater conditions including low visibility, noise, and large areas of featureless scene. To mitigate these problems, we design artificial landmarks to be utilized with a camera for localization, and propose a novel vision-based object detection technique and apply it to the Monte Carlo localization (MCL) algorithm, a map-based localization technique. In the image processing step, a novel correlation coefficient using a weighted sum, multiple-template-based object selection, and color-based image segmentation methods are proposed to improve the conventional approach. In the localization step, to apply the landmark detection results to MCL, dead-reckoning information and landmark detection results are used for prediction and update phases, respectively. The performance of the proposed technique is evaluated by experiments with an underwater robot platform and the results are discussed.     

Semantic aware sport image resizing jointly using seam carving and warping

In content aware image resizing, saliency map or gradient is usually used to determine the important regions of images. But for sport images such as basketball and football images, these methods may falsely classify parts of court fields as unimportant regions, while parts of grandstands as important regions. Such results are not consistent with human perception. In this paper, a semantic aware image resizing approach is proposed. We extract the semantic information automatically. We segment the court fields as important regions and detect the boundary of court fields as the semantic edges. Considering the complementary characteristic of discrete image resizing approaches such as seam carving and continuous approaches such as warping, seam carving and warping are jointly used in our scheme. We define the Semantic Weight Function (SWF) based on the semantically important regions. Then semantic aware seam carving (SASC) is proposed based on the SWF. Next we define the Deformation of Semantic Edges (DSE) to assess the image deformation caused by seam carving. Finally seam carving and warping are joined using the DSE. We compare our approach with approaches like scaling, seam carving and semantic aware seam carving (SASC). Experimental results show that our approach preserves more semantically important regions with less deformation. Our approach also preserves the aspect ratio of key objects.     

Some experiments in relaxation image matching using corner features

A relaxation method based on patterns of local features is used to find matches between pairs of images or subimages that differ in position or orientation. A local operator is applied to the two images to detect two sets of “corners” C₁ …, C_m and D₁, …, D_n, each of them characterized by position, orientation, contrast and “sharpness” (of the angle). For each pair (C_i, D_j), a figure of merit is computed and a relaxation process is used to iteratively adjust these figures of merit, based on the merits of other pairs in approximately corresponding positions. After a few iterations of this process, “good” matches (pairs having much better merit than their next best choices) are clustered, yielding sets of transformation parameters (shift vectors or rotation angles) under which many corners correspond. This method has yielded good results for TV images of objects such as tools and industrial parts, as well as for aerial images of terrain.     

The first stage in two-stage template matching

This paper formulates the problem encountered in the first stage of two-stage, binary template matching as a set of hypotheses to be tested, including a hypothesis of ``no object.' Two new statistics R and G are proposed, based on a likelihood ratio, and are compared to the sum of absolute differences and a correlation measure by analytical approximations and Monte Carlo experiments. Statistical power and a measure of sensitivity to the true location of the object are the criteria. Parameters are the numbers of 1's in object and image, subtemplate size, and parameters reflecting intensity distortion between template and object. One of the proposed statistics R is much more computationally intensive than the other G. Although R is more powerful than G and the other statistics, G is generally more sensitive to the true object location. Statistic G is also more powerful than the sum of absolute differences and correlation. All statistics are robust to incomplete knowledge of distortion parameters. Experiments on Landsat images confirm the sensitivity of G and recommend it for application in the first stage.     

Detection of pre-defined boundaries between hydrothermal alteration zones using rotation-variant template matching

A new method for the detection of pre-defined boundaries in single-band image data that uses a rotation-variant template matching (RTM) algorithm is presented. This algorithm matches a miniature image of a pre-defined boundary to image data at various orientations. The image pixels that match boundary criteria are reported in output imagery together with the rotation angle of the template. The method is applied to identify boundaries between hydrothermal alteration zones in processed airborne hyperspectral imagery, based on the presence of white mica minerals. Results show that boundaries identified with RTM are relatively free of noise and more coherent than those identified with, for instance, image slicing techniques. Identified boundaries can be used for image segmentation. The output of the RTM algorithm also provides information on the type of boundary, whether it is crisp or gradual. This information can be used to better characterize mineral variation in the alteration halo associated with fossil hydrothermal systems.