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...     

Efficient moving average transform-based subsequence matching algorithms in time-series databases

Moving average transform is very useful in finding the trend of time-series data by reducing the effect of noise, and has been used in many areas such as econometrics. Previous subsequence matching methods with moving average transform, however, are problematic in that, since they must build multiple indexes in supporting transform of arbitrary order, they incur index overhead both in storage space and in update maintenance. To solve this problem, we propose a single-index approach to subsequence matching that supports moving average transform of arbitrary order in time-series databases. Using the single-index approach, we can reduce both the storage space and the index maintenance overhead. In explaining the single-index approach, we first introduce the notion of poly-order moving average transform by generalizing the original definition of moving average transform. We then formally prove the correctness of poly-order transform-based subsequence matching. We also propose two subsequence matching methods based on poly-order transform that efficiently support moving average transform of arbitrary order. Experimental results for real stock data show that, compared with the sequential scan, our methods improve average performance significantly, by a factor of 22.6-33.6. Also, compared with cases in which an index is built for every moving average order, our methods reduce storage space and maintenance effort significantly while incurring only marginal performance degradation. Our approach entails the additional advantage of being generalized to support many other transforms in addition to moving average transform. Therefore, we believe that our approach will be widely used in many transform-based subsequence matching methods.     

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...     

Guided-MLESAC: faster image transform estimation by using matching priors

MLESAC is an established algorithm for maximum-likelihood estimation by random sampling consensus, devised for computing multiview entities like the fundamental matrix from correspondences between image features. A shortcoming of the method is that it assumes that little is known about the prior probabilities of the validities of the correspondences. This paper explains the consequences of that omission and describes how the algorithm's theoretical standing and practical performance can be enhanced by deriving estimates of these prior probabilities. Using the priors in guided-MLESAC is found to give an order of magnitude speed increase for problems where the correspondences are described by one image transformation and clutter. This paper describes two further modifications to guided-MLESAC. The first shows how all putative matches, rather than just the best, from a particular feature can be taken forward into the sampling stage, albeit at the expense of additional computation. The second suggests how to propagate the output from one frame forward to successive frames. The additional information makes guided-MLESAC computationally realistic at video-rates for correspondence sets modeled by two transformations and clutter.     

Interactive image segmentation by matching attributed relational graphs

A model-based graph matching approach is proposed for interactive image segmentation. It starts from an over-segmentation of the input image, exploiting color and spatial information among regions to propagate the labels from the regions marked by the user-provided seeds to the entire image. The region merging procedure is performed by matching two graphs: the input graph, representing the entire image; and the model graph, representing only the marked regions. The optimization is based on discrete search using deformed graphs to efficiently evaluate the spatial information. Note that by using a model-based approach, different interactive segmentation problems can be tackled: binary and multi-label segmentation of single images as well as of multiple similar images. Successful results for all these cases are presented, in addition to a comparison between our binary segmentation results and those obtained with state-of-the-art approaches. An implementation is available at http://structuralsegm.sourceforge.net/.     

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.

     

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.     

Interactive image segmentation using probabilistic hypergraphs

This paper introduces a novel interactive framework for segmenting images using probabilistic hypergraphs which model the spatial and appearance relations among image pixels. The probabilistic hypergraph provides us a means to pose image segmentation as a machine learning problem. In particular, we assume that a small set of pixels, which are referred to as seed pixels, are labeled as the object and background. The seed pixels are used to estimate the labels of the unlabeled pixels by learning on a hypergraph via minimizing a quadratic smoothness term formed by a hypergraph Laplacian matrix subject to the known label constraints. We derive a natural probabilistic interpretation of this smoothness term, and provide a detailed discussion on the relation of our method to other hypergraph and graph based learning methods. We also present a front-to-end image segmentation system based on the proposed method, which is shown to achieve promising quantitative and qualitative results on the commonly used GrabCut dataset.     

基于小波变换的快速图像匹配算法与实现

归一化互相关算法是传统的图像匹配算法,针对传统图像匹配算法运算量大、速度慢的缺点,提出了一种基于小波变换的多尺度图像匹配算法.首先在尺度空间的最高一层对低分辨率的子图像进行匹配,然后在匹配结果基础上对高分辨率的图像进行匹配,最终实现全分辨率下的图像匹配.实验结果表明,该算法能够提高图像匹配的精度,减少运算量,满足机器视觉的实时性要求.     

Remote sensing image fusion using the curvelet transform

This paper presents an image fusion method suitable for pan-sharpening of multispectral (MS) bands, based on nonseparable multiresolution analysis (MRA). The low-resolution MS bands are resampled to the fine scale of the panchromatic (Pan) image and sharpened by injecting highpass directional details extracted from the high-resolution Pan image by means of the curvelet transform (CT). CT is a nonseparable MRA, whose basis functions are directional edges with progressively increasing resolution. The advantage of CT with respect to conventional separable MRA, either decimated or not, is twofold. Firstly, directional detail coefficients matching image edges may be preliminarily soft-thresholded to achieve a noise reduction that is better than that obtained in the separable wavelet domain. Secondly, modeling of the relationships between high-resolution detail coefficients of the MS bands and of the Pan image is more fitting, being accomplished in the directional multiresolution domain. Experiments are carried out on very-high-resolution MS + Pan images acquired by the QuickBird and Ikonos satellite systems. Fusion simulations on spatially degraded data, whose original MS bands are available for reference, show that the proposed curvelet-based fusion method performs slightly better than the state-of-the art. Fusion tests at the full scale reveal that an accurate and reliable Pan-sharpening, little affected by local inaccuracies even in the presence of complex and detailed urban landscapes, is achieved by the proposed method.     

Perceptually adaptive spread transform image watermarking scheme using Hadamard transform

The present paper proposes a digital image watermarking scheme using the characteristics of the human visual system (HVS), spread transform technique and statistical information measure. Spread transform (ST) scheme is implemented using the transform coefficients of both the host and the watermark signal. Watermark embedding strength is adaptively adjusted using frequency sensitivity, luminance, contrast and entropy masking of HVS model. The choice of Hadamard transform as watermark embedding domain offers several advantages, such as low loss in image information (higher image fidelity), greater reliability of watermark detection and higher data hiding capacity at high degree of compression. Performance of the proposed method is compared with a number of recently reported watermarking schemes based on spread spectrum (SS) and quantization index modulation (QIM).     

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     

Hyperspectral image classification using graph-based wavelet transform

ABSTRACT

Graph-based methods are developed to efficiently extract data information. In particular, these methods are adopted for high-dimensional data classification by exploiting information residing on weighted graphs. In this paper, we propose a new hyperspectral texture classifier based on graph-based wavelet transform. This recent graph transform allows extracting textural features from a constructed weighted graph using sparse representative pixels of hyperspectral image. Different measurements of spectral similarity between representative pixels are tested to decorrelate close pixels and improve the classification precision. To achieve the hyperspectral texture classification, Support Vector Machine is applied on spectral graph wavelet coefficients. Experimental results obtained by applying the proposed approach on Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) and Reflective Optics System Imaging Spectrometer (ROSIS) datasets provide good accuracy which could exceed 98.7%. Compared to other famous classification methods as conventional deep learning-based methods, the proposed method achieves better classification performance. Results have shown the effectiveness of the method in terms of robustness and accuracy.     

Economic design of autoregressive moving average control chart using genetic algorithms

When designing control charts, it is usually assumed that the observations from the process at different time points are independent. However, this assumption may not be true for some production processes, e.g., the continuous chemical processes. The presence of autocorrelation in the process data can result in significant effect on the statistical performance of control charts. Jiang, Tsui, and Woodall (2000) developed a control chart, called the autoregressive moving average (ARMA) control chart, which has been shown suitable for monitoring a series of autocorrelated data. In the present paper, we develop the economic design of ARMA control chart to determine the optimal values of the test and chart parameters of the chart such that the expected total cost per hour is minimized. An illustrative example is provided and the genetic algorithm is applied to obtain the optimal solution of the economic design. A sensitivity analysis shows that the expected total cost associated with the control chart operation is positively affected by the occurrence frequency of the assignable cause, the time required to discover the assignable cause or to correct the process, and the quality cost per hour while producing in control or out of control, and is negatively influenced by the shift magnitude in process mean.     

Multi-resolution image representation by using polychromatic wavelet transform

This paper proposes a new method for performing multi-resolution image representation by using polychromatic wavelet transform. The proposed method has an advantage in that multi-resolution outputs can be simultaneously obtained. Preliminary experimental results are presented.