14823691
MRF parameter estimation by MCMC method
Abstract Markov random field (MRF) modeling is a popular pattern analysis method and MRF parameter estimation plays an important role in MRF modeling. In this paper, a method based on Markov Chain Monte Carlo (MCMC) is proposed to estimate MRF parameters. Pseudo-likelihood is used to represent likelihood function and it gives a good estimation result.
5903999
Texture classification using multiresolution Markov random field models
Abstract Texture classification is an important topic in texture analysis. Texture classification has wide applications in remote sensing, computer vision, and image analysis. During the past years, several authors discussed to use multiresolution stochastic approaches to model textures. However, in these approaches, the highpass components which contain rich detailed information are lost. In this paper, we propose multiresolution MRF (MRMRF) modeling to describe textures. MRMRF modeling is a method trying to fuse filtering theory and MRF models. In the MRMRF modeling, highpass components are considered as well as lowpass components. “Brodatz texture database” is used in this paper for the experiments and Nearest Linear Combination (NLC) is used as measurement of distance to improve the recognition rate. The experimental results show that NLC has much better performance than Nearest Neighbor (NN) as the measurement in MRMRF modeling.
26794680
Texture segmentation based on MRMRF modeling
Texture segmentation remains a fundamental issue in low-level image analysis, pattern recognition and computer vision. Texture segmentation problem can be solved in two directions: model fitting and non-parametric classification. In this paper, we propose to use multiresolution MRF (MRMRF) modeling in texture segmentation. A novel MRMRF parameter estimation method based on MCMC approach is presented. The experimental result shows that the method is suitable to segment textured images.
14600536
An Approach to Provisioning E-Commerce Applications with Commercial Components
Component-based development is a trend towards building e-commerce applications. However, commercial components are rarely used during the development. The reason is that existing approaches to selecting and composing components suffer from the problem that the components retrieved usually do not exactly fit with other components in the system being developed. While formal methods can be used to describe and check semantic characteristics to better match components, there are practical limitations which restrict their adoption. We have proposed a framework to support a semantic description and selection of components. We used Simple Component Interface Language (SCIL) to describe user requirements and pre-built components from the current component sources. Specifications in SCIL can be translated to a variety of models including those that have a formal basis. In this paper, we perform a case study of searching commercial components for a generic e-commerce application. We specify the commercial components in SCIL and use two specific tools: jMocha and Alloy Analyser to identify the correct components that suit a particular task
20762776
A Symmetric Orthogonal FastICA Algorithm and Applications in EEG
Extracting a number of independent components,FastICA algorithm can ensure that, each component extracted has never been extracted by adding orthogonalization steps. However, Gram-Schmidt orthogonal method implemented itself that estimation error of the first vector has been accumulated in the subsequent vectors. In this paper, the square root of the classical matrix method is used to achieve symmetric orthogonal that parallel to estimate the source of variables and avoid the Gram-Schmidt orthogonal method of estimation error accumulation problem. The symmetric orthogonal FastICA algorithm is apply in removal EEG artifacts (including eye movement, blinking, ECG, EMG, etc). The experiment and simulation results demonstrate that the symmetry orthogonal FastICA algorithm removes the artifacts and has a better separation efficiency and fast convergence.
11066290
Robust multi-level video representation using mean shift analysis
A robust method for multi-level video representation based on the mean shift analysis (MSA) of low-level visual features is proposed in this paper. By tuning the bandwidth of MSA, video representation from the coarse level to the fine level can be achieved. This representation form provides a flexible scheme for content-based video analysis such as summarization, classification, and retrieval. Compared with the conventional k-means or fuzzy c-means algorithms, our method can adjust the resolution of representation in a more straightforward way, and is more robust since it does not need to initialize the cluster centers
30078088
Image Segmentation by a Robust Clustering Algorithm Using Gaussian Estimator
This paper presents a novel clustering-based image segmentation method, which incorporates the features of robust statistics. To overcome the sensitivity to noise and outliers in fuzzy clustering, a simple but efficient M-estimator, Gaussian estimator, has been introduced to clustering analysis as weight or membership function. When applied to image analysis, the proposed Robust Gaussian Clustering/Segmentation (RGCS) algorithm exhibits more reasonable pixel classification and noise suppression performance with respect to FCM. Moreover, by using a uniform resolution parameter scheme, this method avoids producing coincident clusters, which occurs often in possibilistic clustering segmentation. Experiments on both the synthetic data and real image demonstrate the validity and power of the proposed algorithm.
7234494
A novel method for a class of nonlinear singular perturbation problems
In this paper, we present a boundary value method for solving a class of nonlinear, singularly perturbed two-point boundary value problems with a boundary layer at the left of the underlying interval. This method is based on ideas of singular perturbation analysis by constructing a modified problem with a boundary layer correction. We deal with the boundary layer separately, and use series method. The condition at infinity will be applied to the corresponding Pade approximates of the obtained series solution.