A Method for Calligraphy Writer Identification by Integrating Gabor Filter and Gaussian Markov Random Field

A new adaptive filtering algorithm in the undecimated wavelet domain based on several adjacent scales correlation is proposed in this paper. Moreover, the Gaussian Markov random field (GMRF) model is used to characterize the inner- scale contextual dependence of wavelet coefficients. In the end, both the simulated speckle images and the real SAR images are used to validate the performance of our new algorithm. (5 pages)

This paper proposes a GMRF (Gaussian Markov random field)-model based restoration method of missing areas in still images. The GMRF model used in the proposed method is realized by a new assumption that reasonably holds for an image source. This model can express important image features such as edges because of the use of the new assumption. Therefore, the proposed method restores the missing areas using the modified GMRF model and can correctly reconstruct the missing edges. Consequently, the proposed method achieves more accurate restoration than those of the traditional methods on both objective and subjective measures. Extensive experimental results demonstrate the improvement of the proposed method over previous methods.

A stochastic model-based approach is presented for estimation of the two-dimensional maximum entropy power spectrum (MEPS) from given finite uniform array data. The method consists of fitting an appropriate two-dimensional noncausal Gaussian-Markov random field (GMRF) model to the given data using the maximum likelihood (ML) technique for parameter estimation. The nonlinear criterion function used for ML estimation is similar in structure to the function arising in the deterministic approach of Lang and McClellan. The model-based approach provides new insights into the two-dimensional MEPS estimation problem. For example, using the asymptotic normality of ML estimates, we derive simultaneous confidence bands for the estimated MEPS. It turns out that when the true correlations are generated by a noncausal GMRF model, the two-dimensional MEPS can be obtained by solving linear equations. This approach also suggests techniques for realizing two-dimensional GMRF models from the given correlation data. Several numerical examples are given to illustrate the usefulness of the approach.

This paper is concerned with a systematic exposition of the usefulness of two-dimensional (2-D) discrete Gaussian Markov random field (GMRF) models for image processing applications. Specifically, we discuss the following topics; notion of Markovianity on a plane, statistical inference in GMRF models, and their applications in several image related problems such as, image synthesis, texture classification, segmentation and image restoration.

This paper proposes a Markov Random Field (MRF) model used for denoising to preserve the edge. This model combines both gray distinction and edge detection results to determine the weight between adjacent pixels. On the basis of MMRF (Membrane MRF), it imports the weight coefficient to build EPMRF (Edge Preserving MRF) model. This model can describe edge characteristics effectively and reduce impact between adjacent pixels to achieve preserving the edge. The experimental results show that denoising based on EPMRF model have a better performance compared with the traditional MMRF and GMRF (Gaussian MRF) models in removing the image noise, especially in edge preserving.

Modeling textures using Gaussian Markov random fields (GMRF) has been successfully used in classifying textures. However, these models do not perform well for self-similar textures such as those generated from fractional Brownian motion. The authors show that by using the difference images at different scales instead of the original image, one can significantly increase the performance of classifying self-similar texture patterns using GMRF models. >

Applications of Noncausal Gauss–Markov Random Field Models in Image and Video Processing

This paper proposes a method for texture feature extraction by integrating Gabor filters and independent component analysis (ICA) for Uyghur handwriting based writer identification. That is, the texture image is firstly filtered by a given bank of Gabor filters, and then higher dimensional feature vectors are constructed from the filtered texture images. Next, the dimensionality of these vectors is reduced by means of principal component analysis (PCA). Finally, the independent components in the resulting vectors with dimensionality reduced are analyzed and extracted by us. Experiments were performed using KNN-5 classifier to Uyghur handwriting samples from 55 different people and promising results of 92.5% correct identification rate were achieved.

Anomaly detection is of great importance both in civil and military applications. To avoid the interference of complex background, we propose a texture segmentation based anomaly detection algorithm (TBAD). TBAD introduces the Gaussian Markov random field (GMRF) to model the distribution of background pixel values. Through the GMRF model, images are segmented into series of textures. And the conventional RX algorithm is applied on each segmented textures. Since TBAD can utilize both the spatial and the spectrum information of background, it can reduce the interference of complex background. Experiment applied to real image has validated the performance of the new approach.

The discrimination ability of texture features derived from Gaussian Markov random fields (GMRFs) and grey level co-occurrence probabilities (GLCPs) are compared and contrasted. More specifically, the role of window size in feature consistency and separability as well as the role of multiple textures within a window are investigated. GLCPs are demonstrated to have improved discrimination ability relative to MRFs with decreasing window size, an important concept when performing image segmentation. On the other hand, GLCPs are more sensitive to texture boundary confusion than GMRFs.

This paper presents a novel method for extracting road from high resolution remote sensing image based on Gaussian mixture model and Markov random field (GMM-MRF) model optimized by the iterated conditional model (ICM). In this paper, we first divide remote sensing image into 3 classes by GMM-MRF texture segmentation model, and then extract road by fuzzy connectedness. Finally, we use shape features and mathematical morphology to do the post-processing to remove false alarm area. Since fuzzy connectedness lacks regional structure factors, it can not accurately describe the pixel similarity. By introducing image texture feature to the concept of fuzzy connectedness, we can establish texture fuzzy connectedness concept to make it more accurate description of the degree of association among pixels, which reflects the characteristics of the local and regional pixel similarity. Besides, our method choose only one road seed point and can achieve better road extraction effectiveness than fuzzy connectedness without using segmentation algorithms, which usually needs more than 15 seed points. Experiments show that the proposed method can effectively eliminate the non-road areas that whose characteristics are similar to road, and it can obtain better performance for road extraction from high resolution remote sensing image compared with fuzzy c-means and mathematical morphology.

Abstract. Digital Elevation Models (DEMs) are considered as one of the most relevant geospatial data to carry out land-cover and land-use classification. This work deals with the application of a mathematical framework based on a Gaussian Markov Random Field (GMRF) to interpolate grid DEMs from scattered elevation data. The performance of the GMRF interpolation model was tested on a set of LiDAR data (0.87 points/m2) provided by the Spanish Government (PNOA Programme) over a complex working area mainly covered by greenhouses in Almería, Spain. The original LiDAR data was decimated by randomly removing different fractions of the original points (from 10% to up to 99% of points removed). In every case, the remaining points (scattered observed points) were used to obtain a 1 m grid spacing GMRF-interpolated Digital Surface Model (DSM) whose accuracy was assessed by means of the set of previously extracted checkpoints. The GMRF accuracy results were compared with those provided by the widely known Triangulation with Linear Interpolation (TLI). Finally, the GMRF method was applied to a real-world case consisting of filling the LiDAR-derived DSM gaps after manually filtering out non-ground points to obtain a Digital Terrain Model (DTM). Regarding accuracy, both GMRF and TLI produced visually pleasing and similar results in terms of vertical accuracy. As an added bonus, the GMRF mathematical framework makes possible to both retrieve the estimated uncertainty for every interpolated elevation point (the DEM uncertainty) and include break lines or terrain discontinuities between adjacent cells to produce higher quality DTMs.

Basic textures as they appear, especially in high resolution SAR images, are affected by multiplicative speckle noise and should be preserved by despeckling algorithms. Sharp edges between different regions and strong scatterers also must be preserved. To despeckle images, the authors use a maximum aposteriori (MAP) estimation of the cross section, choosing between different prior models. The proposed approach uses a Gauss Markov random field (GMRF) model for textured areas and allows an adaptive neighborhood system for edge preservation between uniform areas. In order to obtain the best possible texture reconstruction, an expectation maximization algorithm is used to estimate the texture parameters that provide the highest evidence. Borders between homogeneous areas are detected with a stochastic region-growing algorithm, locally determining the neighborhood system of the Gauss Markov prior. Smoothed strong scatterers are found in the ratio image of the data and the filtering result and are replaced in the image. In this way, texture, edges between homogeneous regions, and strong scatterers are well reconstructed and preserved. Additionally, the estimated model parameters can be used for further image interpretation methods.

Previously reported classification or segmentation methods are reviewed, and some statistical approaches that may be capable of automatically classifying tissues and segmenting magnetic resonance (MR) images are discussed. The image segmentation methods reviewed are edge detection methods and region detection methods. The key feature of statistical approaches toward automatically classifying tissues and segmenting MR images is the determination of the number of image classes and the model parameters of these classes from the image data directly by a computer. Any free parameter requiring extensive user interactions should be avoided. Further research on the Gaussian Markov random field (GMRF) model and the MRF penalty term will push the statistical approaches further along the automatic track. As these approaches become more practical they will become more valuable.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

This paper presents multiresolution models for Gauss-Markov random fields (GMRFs) with applications to texture segmentation. Coarser resolution sample fields are obtained by subsampling the sample field at fine resolution. Although the Markov property is lost under such resolution transformation, coarse resolution non-Markov random fields can be effectively approximated by Markov fields. We present two techniques to estimate the GMRF parameters at coarser resolutions from the fine resolution parameters, one by minimizing the Kullback-Leibler distance and another based on local conditional distribution invariance. We also allude to the fact that different GMRF parameters at the fine resolution can result in the same probability measure after subsampling and present the results for first- and second-order cases. We apply this multiresolution model to texture segmentation. Different texture regions in an image are modeled by GMRFs and the associated parameters are assumed to be known. Parameters at lower resolutions are estimated from the fine resolution parameters. The coarsest resolution data is first segmented and the segmentation results are propagated upward to the finer resolution. We use the iterated conditional mode (ICM) minimization at all resolutions. Our experiments with synthetic, Brodatz texture, and real satellite images show that the multiresolution technique results in a better segmentation and requires lesser computation than the single resolution algorithm.