Adaptive Gaussian Kernel Research Articles

Target Segmentation in Non-homogeneous Infrared Images using a PCA Plane and an Adaptive Gaussian Kernel

We propose an efficient method of extracting targets within a region of interest in non-homogeneous infrared images by using a principal component analysis (PCA) plane and adaptive Gaussian kernel. Existing approaches for extracting targets have been limited to using only the intensity values of the pixels in a target region. However, it is difficult to extract the target regions effectively because the intensity values of the target region are mixed with the background intensity values. To overcome this problem, we propose a novel PCA based approach consisting of three steps. In the first step, we apply a PCA technique minimizing the total least-square errors of an IR image. In the second step, we generate a binary image that consists of pixels with higher values than the plane, and then calculate the second derivative of the sum of the square errors (SDSSE). In the final step, an iteration is performed until the convergence criteria is met, including the SDSSE, angle and labeling value. Therefore, a Gaussian kernel is weighted in addition to the PCA plane with the non-removed data from the previous step. Experimental results show that the proposed method achieves better segmentation performance than the existing method.

An Adaptive Spectral Clustering Algorithm Based on the Importance of Shared Nearest Neighbors

The construction of a similarity matrix is one significant step for the spectral clustering algorithm; while the Gaussian kernel function is one of the most common measures for constructing the similarity matrix. However, with a fixed scaling parameter, the similarity between two data points is not adaptive and appropriate for multi-scale datasets. In this paper, through quantitating the value of the importance for each vertex of the similarity graph, the Gaussian kernel function is scaled, and an adaptive Gaussian kernel similarity measure is proposed. Then, an adaptive spectral clustering algorithm is gotten based on the importance of shared nearest neighbors. The idea is that the greater the importance of the shared neighbors between two vertexes, the more possible it is that these two vertexes belong to the same cluster; and the importance value of the shared neighbors is obtained with an iterative method, which considers both the local structural information and the distance similarity information, so as to improve the algorithm’s performance. Experimental results on different datasets show that our spectral clustering algorithm outperforms the other spectral clustering algorithms, such as the self-tuning spectral clustering and the adaptive spectral clustering based on shared nearest neighbors in clustering accuracy on most datasets.

Online clutter estimation using a Gaussian kernel density estimator for multitarget tracking

In this study, the spatial distribution of false alarms is assumed to be a non-homogeneous Poisson point (NHPP) process. Then, a new method is developed under the kernel density estimation (KDE) framework to estimate the spatial intensity of false alarms for the multitarget tracking problem. In the proposed method, the false alarm spatial intensity estimation problem is decomposed into two subproblems: (i) estimating the number of false alarms in one scan and (ii) estimating the variation of the intensity function value in the measurement space. Under the NHPP assumption, the only parameter that needs to be estimated for the first subproblem is the mean of false alarm number, and the empirical mean is used here as the maximum likelihood estimate of that parameter. Then, for the second subproblem, an online multivariate local adaptive Gaussian kernel density estimator is proposed. Furthermore, the proposed estimation method is seamlessly integrated with widely used multitarget trackers, like the joint integrated probabilistic data association algorithm and the multiple hypotheses tracking algorithm. Simulation results show that the proposed KDE-based method can provide a better estimate of the false alarm spatial intensity and help the multitarget trackers yield superior performance in scenarios with spatially non-homogeneous false alarms.

A new family of Gaussian filters with adaptive lobe location and smoothing strength for efficient image restoration

Noise can occur during image capture, transmission, or processing phases. Image de-noising is a very important step in image processing, and many approaches are developed in order to achieve this goal such as the Gaussian filter which is efficient in noise removal. Its smoothing efficiency depends on the value of its standard deviation. The mask representing the filter presents generally static weights with invariant lobe. In this paper, an adaptive de-noising approach is proposed. The proposed approach uses a Gaussian kernel with variable width and direction called adaptive Gaussian kernel (AGK). In each processed window of the image, the smoothing strength changes according to the image content, noise kind, and intensity. In addition, the location of its lobe changes in eight different directions over the processed window. This directional variability avoids averaging details by the highest mask weights in order to preserve the edges and the borders. The recovered data is de-noised efficiently without introducing blur or losing details. A comparative study with the static Gaussian filter and other recent techniques is presented to prove the efficiency of the proposed approach.

Surface-based automatic coarse registration of head scans.

Surface registration is widely used in image-guided neurosurgery to achieve spatial registration between the patient space and the image space. Coarse registration, followed by fine registration, is an important premise to ensure the robustness and efficiency of surface registration. In this paper, a coarse registration algorithm based on the principal axes is proposed to achieve this goal. The extraction of the principal axes relies on the approximated surface with an adaptive Gaussian kernel, the width of which is consistent with neighborhood relation so that it is applicable for various scanning data. Determining the corresponding centers of translation is another problem for aligning different scanning data, which is solved through heuristics. Six pairs of points on two surfaces with the farthest projections on the principal axes were regarded as the candidates of translation centers, and then through tentative alignments of local regions around them, a pair of candidates with the minimum registration error was selected as the optimal translation centers. Automatic registration of two scans of a head phantom is presented in this paper. Experimental results confirmed the robustness of the algorithm and its feasibility in clinical applications.

Feature extraction in time-frequency analysis of radar signal sorting

According to the high complexity of extracting feature of radar signal using image processing method,a new method for extracting feature was proposed.Firstly,the time-frequency distribution was gained based on the adaptive Gaussian kernel time frequency analysis,then through analyzing the physical meaning of each element,one dimension vector could be found through a simple arithmetic instead of the complicated method through processing the time-frequency figure with image processing means,so the real-time requirement for sorting radar signal could be satisfied.The simulation results verify the efficiency of the proposed algorithm.Additionally,the accuracy can be kept at a high level while the Signal-to-Noise Ratio(SNR) is low.

SPATIAL SMALL AREA ESTIMATION FOR DETERMINATION OF UNDERDEVELOPED VILLAGES IN THE PROVINCE OF YOGYAKARTA (DIY) IN 2011

Indonesia's poverty alleviation programs are implemented by two approaches target, those are the pockets (areas) of poverty and the poor households. Related with poverty alleviation programs targeting poor areas, in the Medium Term Development Plan (RPJM) 2010-2014, the government through the Development Backward Areas Ministry (KPDT) has determined the backward or underdeveloped regions at the level of district/city. There is no district/city in the province of Yogyakarta (DIY) are classified as underdeveloped region, but in 2011 the poverty rate in DIY is the highest compared with other provinces in Java and Bali. Therefore, the classifications of underdeveloped areas are not optimal if applicable only within the district, but it needs to be seen in the smaller scope, such as village. The main purpose of this study is to determine the underdeveloped villages in DIY in 2011. The average per capita household expenditure is a key indicator in measuring poverty. Susenas data can only be used to estimate the average per capita household expenditure to the level of district. Therefore, to obtain the estimated value in village level, this study used Small Area Estimation approach by combining census data (Podes 2011) and survey data (Susenas 2011). This study used Geographically Weighted Regression (GWR) with Adaptive Gaussian Kernel Bandwidth weighting function. GWR is a linear regression model that produces the local parameters in all locations. GWR parameters estimated are performed by Weighted Least Squares (WLS) method which involving spatial aspects. The results found that there were 13 underdeveloped villages in DIY. Furthermore, the Local Indicator of Spatial Association (LISA) is used to look at the tendency of cluster in underdeveloped villages. Then, maps are used to compare characteristic of underdeveloped villages among others.

Adaptive Spatiotemporal Smoothing of Seismicity for Long-Term Earthquake Forecasts in California

We present new methods for time‐independent earthquake forecasting that employ space–time kernels to smooth seismicity. The major advantage of the methods is that they do not require prior declustering of the catalog, circumventing the relatively subjective choice of a declustering algorithm. Past earthquakes are smoothed in space and time using adaptive Gaussian kernels. The bandwidths in space and time associated with each event are a decreasing function of the seismicity rate at the time and location of each earthquake. This yields a better resolution in space–time volumes of intense seismicity and a smoother density in volumes of sparse seismicity. The long‐term rate in each spatial cell is then defined as the median value of the temporal history of the smoothed seismicity rate in this cell. To calibrate the model, the earthquake catalog is divided into two parts: the early part (the learning catalog) is used to estimate the model, and the latter one (the target catalog) is used to compute the likelihood of the model’s forecast. We optimize the model’s parameters by maximizing the likelihood of the target catalog. To estimate the kernel bandwidths in space and time, we compared two approaches: a coupled near‐neighbor method and an iterative method based on a pilot density. We applied these methods to Californian seismicity and compared the resulting forecasts with our previous method based on spatially smoothing a declustered catalog (Werner et al. , 2011). All models use small M ≥2 earthquakes to forecast the rate of larger earthquakes and use the same learning catalog. Our new preferred model slightly outperforms our previous forecast, providing a probability gain per earthquake of about 5 relative to a spatially uniform forecast.

Feedforward Analysis for Shield-Ground System

AbstractGround surface settlement is an important measurement in identifying potential damages for shield tunneling. Identifying the relationship between shield parameters and the resulting settlement is of vital importance to the reasonable adjustment of the shield parameters so as to control settlement development. However, many other factors, besides the shield parameters, affect settlement, which makes shield-ground interaction complicated. Therefore, a better method is necessary for extracting the shield-ground relationship for the purpose of steering shield tunneling. This paper proposes a method that incorporates smooth relevance vector machine (sRVM) and particle swarm optimization (PSO) for shield steering with concern for settlement control. First, smooth relevance vector machine with adaptive Gaussian kernel function is used to establish the relationship between the identified factors and the settlement. Particle swarm optimization is then applied to identify the appropriate kernel parameters. ...

Relevance vector machine with adaptive wavelet kernels for efficient image coding

This paper presents a practical and effective image compression system based on wavelet decomposition and RVM regression for compressing still images. Support vector machine (SVM)-based approaches have been recently proposed for image compression and have raised important interest. In this paper, it is genuinely proposed to use an RVM-based approach for the compression of color images. Since RVMs performance depends to a large degree on the choice of a kernel and kernel parameters, RVM with adaptive wavelet kernels (Adaptive WRVM) is proposed to improve the compression performance of RVM with standard wavelet kernels (Standard WRVM) for image coding. Comparative study of adaptive wavelet kernels and Gaussian kernel is carried out and results showed that adaptive Mexican hat wavelet kernel achieves the best image quality at a given compression ratio. A performance comparison of proposed algorithm with Rki-1, SVM with wavelet kernels (WSVM) and JPEG2000 compression systems is done. It is found that proposed algorithm gives better image quality for a given compression rate in comparison to Rki-1, SVM with wavelet kernels (WSVM) and comparable to JPEG2000.

Adaptive spherical Gaussian kernel in sparse Bayesian learning framework for nonlinear regression

Kernel based machine learning techniques have been widely used to tackle problems of function approximation and regression estimation. Relevance vector machine (RVM) has state of the art performance in sparse regression. As a popular and competent kernel function in machine learning, conventional Gaussian kernel has unified kernel width with each of basis functions, which make impliedly a basic assumption: the response is represented below certain frequency and the noise is represented above such certain frequency. However, in many case, this assumption does not hold. To overcome this limitation, a novel adaptive spherical Gaussian kernel is utilized for nonlinear regression, and the stagewise optimization algorithm for maximizing Bayesian evidence in sparse Bayesian learning framework is proposed for model selection. Extensive empirical study, on two artificial datasets and two real-world benchmark datasets, shows its effectiveness and flexibility of model on representing regression problem with higher levels of sparsity and better performance than classical RVM. The attractive ability of this approach is to automatically choose the right kernel widths locally fitting RVs from the training dataset, which could keep right level smoothing at each scale of signal.

Vector-valued image regularization with PDEs: a common framework for different applications

In this paper, we focus on techniques for vector-valued image regularization, based on variational methods and PDEs. Starting from the study of PDE-based formalisms previously proposed in the literature for the regularization of scalar and vector-valued data, we propose a unifying expression that gathers the majority of these previous frameworks into a single generic anisotropic diffusion equation. On one hand, the resulting expression provides a simple interpretation of the regularization process in terms of local filtering with spatially adaptive Gaussian kernels. On the other hand, it naturally disassembles any regularization scheme into the smoothing process itself and the underlying geometry that drives the smoothing. Thus, we can easily specialize our generic expression into different regularization PDEs that fulfill desired smoothing behaviors, depending on the considered application: image restoration, inpainting, magnification, flow visualization, etc. Specific numerical schemes are also proposed, allowing us to implement our regularization framework with accuracy by taking the local filtering properties of the proposed equations into account. Finally, we illustrate the wide range of applications handled by our selected anisotropic diffusion equations with application results on color images.