A time-series matching approach for symmetric-invariant boundary image matching

Multimedia Tools and Applications - In this paper, we address the problem of boundary image matching that supports symmetric invariance. Supporting the symmetric invariance is an important factor...     

Interactive noise-controlled boundary image matching using the time-series moving average transform

In this paper we propose a time-series matching-based approach that provides the interactive boundary image matching with noise control for a large-scale image database. To achieve the noise reduction effect in boundary image matching, we exploit the moving average transform of time-series matching. We are motivated by a simple intuition that the moving average transform might reduce the noise of boundary images as well as that of time-series data. To confirm this intuition, we first propose a new notion of k-order image matching, which applies the moving average transform to boundary image matching. A boundary image can be represented as a sequence in the time-series domain, and our k-order image matching identifies similar boundary images in this time-series domain by comparing the k-moving average transformed sequences. We then propose an index-based method that efficiently performs k-order image matching on a large image database, and formally prove its correctness. We also formally analyze the relationship of orders and their matching results and present an interactive approach of controlling the noise reduction effect. Experimental results show that our k-order image matching exploits the noise reduction effect well, and our index-based method outperforms the sequential scan by one or two orders of magnitude. These results indicate that our k-order image matching and its index-based solution provide a very practical way of realizing the noise control boundary image matching. To our best knowledge, the proposed interactive approach for large-scale image databases is the first attempt to solve the noise control problem in the time-series domain rather than the image domain by exploiting the efficient time-series matching techniques. Thus, our approach can be widely used in removing other types of distortions in image matching areas.     

Similar sequence matching supporting variable-length and variable-tolerance continuous queries on time-series data stream

We propose a new similar sequence matching method that efficiently supports variable-length and variable-tolerance continuous query sequences on time-series data stream. Earlier methods do not support variable lengths or variable tolerances adequately for continuous query sequences if there are too many query sequences registered to handle in main memory. To support variable-length query sequences, we use the window construction mechanism that divides long sequences into smaller windows for indexing and searching the sequences. To support variable-tolerance query sequences, we present a new notion of intervaled sequences whose individual entries are an interval of real numbers rather than a real number itself. We also propose a new similar sequence matching method based on these notions, and then, formally prove correctness of the method. In addition, we show that our method has the prematching characteristic, which finds future candidates of similar sequences in advance. Experimental results show that our method outperforms the naive one by 2.6-102.1 times and the existing methods in the literature by 1.4-9.8 times over the entire ranges of parameters tested when the query selectivities are low (<32%), which are practically useful in large database applications.     

Fractional partial differential equation denoising models for texture image

In this paper,a set of fractional partial differential equations based on fractional total variation and fractional steepest descent approach are proposed to address the problem of traditional drawbacks of PM and ROF multi-scale denoising for texture image.By extending Green,Gauss,Stokes and Euler-Lagrange formulas to fractional field,we can find that the integer formulas are just their special case of fractional ones.In order to improve the denoising capability,we proposed 4 fractional partial differential equation based multiscale denoising models,and then discussed their stabilities and convergence rate.Theoretic deduction and experimental evaluation demonstrate the stability and astringency of fractional steepest descent approach,and fractional nonlinearly multi-scale denoising capability and best value of parameters are discussed also.The experiments results prove that the ability for preserving high-frequency edge and complex texture information of the proposed denoising models are obviously superior to traditional integral based algorithms,especially for texture detail rich images.     

Adaptive image denoising using cuckoo algorithm

This paper presents a novel denoising approach based on smoothing linear and nonlinear filters combined with an optimization algorithm. The optimization algorithm used was cuckoo search algorithm and is employed to determine the optimal sequence of filters for each kind of noise. Noises that would be eliminated form images using the proposed approach including Gaussian, speckle, and salt and pepper noise. The denoising behaviour of nonlinear filters and wavelet shrinkage threshold methods have also been analysed and compared with the proposed approach. Results show the robustness of the proposed filter when compared with the state-of-the-art methods in terms of peak signal-to-noise ratio and image quality index. Furthermore, a comparative analysis is provided between the said optimization algorithm and the genetic algorithm.     

Efficient time-series subsequence matching using duality in constructing windows

In this paper, we propose a new subsequence matching method, Dual Match. Dual Match exploits duality in constructing windows and significantly improves performance. Dual Match divides data sequences into disjoint windows and the query sequence into sliding windows, and thus, is a dual approach of the one by Faloutsos et al. (Proceedings of the ACM SIGMOD International Conference on Management of Data, Seattle, Washington, 1994, pp. 419–429.) (FRM in short), which divides data sequences into sliding windows and the query sequence into disjoint windows. FRM causes a lot of false alarms (i.e., candidates that do not qualify) by storing minimum bounding rectangles rather than individual points representing windows to save storage space for the index. Dual Match solves this problem by directly storing points without incurring excessive storage overhead. Experimental results show that, in most cases, Dual Match provides large improvement both in false alarms and performance over FRM given the same amount of storage space. In particular, for low selectivities (less than 10⁻⁴), Dual Match significantly improves performance up to 430-fold. On the other hand, for high selectivities (more than 10⁻²), it shows a very minor degradation (less than 29%). For selectivities in between (10⁻⁴–10⁻²), Dual Match shows performance slightly better than that of FRM. Overall, these results indicate that our approach provides a new paradigm in subsequence matching that improves performance significantly in large database applications.     

Helly hypergraph based matching framework using deterministic sampling techniques for spatially improved point feature based image matching

Multimedia Tools and Applications - Hypergraphs are tools for matching of point-features incorporating spatial relationships in the form of hyperedges exhibiting topological and geometric features...     

基于分数阶偏微分方程的彩色图像去噪新方法

为了克服现有彩色图像去噪方法不能有效抑制边缘噪声和保持纹理信息的缺点, 提出了一种结合了离散四元数傅里叶变换（DQFT）和分数阶微分理论的彩色图像去噪改进方法。算法采用四元数矩阵表示一幅彩色图像, 首先对该四元数矩阵进行离散傅里叶变换; 然后将其代入基于分数阶微分的能量泛函极小值求解过程中, 利用变分原理求解并推导出去噪模型, 并与两种传统彩色图像去噪模型进行实验比较。实验结果表明, 提出的模型在去噪效果和纹理保护方面都有更好的表现。     

Shape matching using relaxation techniques

The problem of finding approximate matches of pieces of shapes to parts of larger shapes is investigated. The shapes are represented by polygonal approximations. Initially, figures of merit are assigned to the matches between pairs of angles on the two shapes. Relaxation methods are then used to find acceptable combinations of these matches. This approach was tested on a data base consisting of digitized coastlines in various map projections. In nearly all cases, all matches except the correct one were eliminated by the relaxation processes.     

基于分数阶偏微分方程的图像去噪新模型

将分数阶微分理论和全变分方法相结合应用于图像去噪,提出了一种基于分数阶偏微分方程的图像去噪新模型。该模型很好地继承了现有的全变分(TV)模型去噪效果与保持图像边缘细节特征的优点,同时利用分数阶微分运算特有的幅频特性优势,较好地保留了图像平滑区域中灰度变化不大的纹理细节。实验结果表明：一方面,与现有去噪方法相比,新模型不仅具有较强的抑制噪声能力,而且能较好地保持图像边缘特征,还能保留更多的图像纹理细节信息,优于常用的整数阶偏微分图像去噪方法;另一方面,从峰值信噪比的对比实验可以看出该模型去噪效果优于其他方法,较好地达到了去噪目的,是一种有效、实用的图像去噪模型。     

基于偏微分方程的图像去噪综合模型

介绍了基于偏微分方程（PDE）的两种去噪模型,即ROF模型和LLT模型。根据对这两种模型的比较,提出了应用权函数来合并ROF模型和LLT模型的综合模型。实验表明,综合模型既能克服ROF模型和LLT模型的缺点,又能融合它们的优点,在去噪、保护平滑区域、保护边缘和纹理细节方面都有较好的表现。     

A hybrid shape-based image clustering using time-series analysis

Clustering of different shapes of the same object has an inordinate impact on various domains, including biometrics, medical science, biomedical signal analysis, and forecasting, for the analysis of huge volume of data into different groups. In this work, we present a novel shape-based image clustering approach using time-series analysis, to guarantee the robustness over the conventional clustering techniques. To evaluate the performance of the proposed procedure, we employed a dataset consists of various real-world irregular shaped objects. The shapes of different objects are first extracted from the entire dataset based on similar pattern using mean structural similarity index. Furthermore, we performed radical scan on the extracted shapes for converting them to one-dimensional (1D) time-series data. Finally, the time series are clustered to form subgroups using hierarchical divisive clustering approach with average linkage, and Pearson as distance metrics. A comparative study with other conventional distance metrices was also conducted. The results established the superiority of using Pearson correlation measure, which provided the maximum F1-score with exact number of shapes under a sub-cluster, while the corresponding outcomes of other approaches results in a poor and inappropriate clustering.

     

Adaptive edge-preserving image denoising using wavelet transforms

Image denoising is a relevant issue found in diverse image processing and computer vision problems. It is a challenge to preserve important features, such as edges, corners and other sharp structures, during the denoising process. Wavelet transforms have been widely used for image denoising since they provide a suitable basis for separating noisy signal from the image signal. This paper describes a novel image denoising method based on wavelet transforms to preserve edges. The decomposition is performed by dividing the image into a set of blocks and transforming the data into the wavelet domain. An adaptive thresholding scheme based on edge strength is used to effectively reduce noise while preserving important features of the original image. Experimental results, compared to other approaches, demonstrate that the proposed method is suitable for different classes of images contaminated by Gaussian noise.     

Distortion-free predictive streaming time-series matching

Efficient processing of streaming time-series generated by remote sensors and mobile devices has become an important research area. As in traditional time-series applications, similarity matching on streaming time-series is also an essential research issue. To obtain more accurate similarity search results in many time-series applications, preprocessing is performed on the time-series before they are compared. The preprocessing removes distortions such as offset translation, amplitude scaling, linear trends, and noise inherent in time-series. In this paper, we propose an algorithm for distortion-free predictive streaming time-series matching. Similarity matching on streaming time-series is saliently different from traditional time-series in that it is not feasible to directly apply the traditional algorithms for streaming time-series. Our algorithm is distortion-free in the sense that it performs preprocessing on streaming time-series to remove offset translation and amplitude scaling distortions at the same time. Our algorithm is also predictive, since it performs streaming time-series matching against the predicted most recent subsequences in the near future, and thus improves search performance. To the best of our knowledge, no streaming time-series matching algorithm currently performs preprocessing and predicts future search results simultaneously.     

A blending method based on partial differential equations for image denoising

In this paper we proposed a new de-noising technique based on combination of isotropic diffusion model, anisotropic diffusion (PM) model, and total variation model. The proposed model is able to be adaptive in each region depending on the information of the image. More precisely, the model performs more diffusion in the flat areas of the image, and less diffusion in the edges of the image. And so we can get rid of the noise, and preserve the edges of the image simultaneously. To verify that, we did several experiments, which showed that our algorithm is the best method for edge preserving and noise removing, compared with the isotropic diffusion, anisotropic diffusion, and total variation methods.     

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.     

基于NSCT域邻域收缩的SAR图像去噪

针对合成孔径雷达（Synthetic Aperture Radar,SAR）图像受到相干斑噪声的干扰,严重影响了SAR图像的后续处理的问题,提出一种在非下采样轮廓变换（Nonsubsampled Contourlet Transform,NSCT）域将中值滤波和邻域收缩法相结合的SAR图像去噪算法。该算法对原始SAR图像进行NSCT分解,得到低频子带和高频子带图像,对低频子带使用中值滤波处理以去除低频子带中的低频噪声,利用NSCT分解系数之间的相关性,使用邻域收缩法对子带图的系数进行收缩,以消除高频子带中的高频噪声。实验证明,该算法与小波域邻域收缩去噪算法和NSCT硬阈值去噪算法相比,在去噪性能和视觉效果方面均有所提高,在消除噪声同时可以较好地保护纹理细节信息。