Multiresolution Markov Random Field Research Articles

Development and Application of Information Resources for Education and Teaching of Yarn History Based on 5G Network Technology

Yarn education is a crucial step in producing high-quality textile end products. By offering quick insights into yarn quality, online yarn testing can reduce latency in critical process control, resulting in the manufacture of higher-quality yarns. This paper proposes the development and application of information resources for the education and teaching of yarn history based on fifth-generation (5G) network technologies. Firstly, the yarn database is preprocessed using greyscale transformation and image filtering methods. Secondly, yarn segmentation is implemented using a Multiresolution Markov Random Field (MRMRF) model. Fourier transform and two-scale attention models are used to extract statistical and relative characteristics, respectively. For classifying the yarn photos, we propose using Improved Support Vector Machine (ISVM) classification. The 5G network has been set up, and data is transferred using the Transmission Control Protocol. To enhance the performance, we propose an Improved Particle Swarm Optimization (IPSO) algorithm. The performance of the suggested methodology is analyzed and compared with existing methodologies using the MATLAB simulation tool. The suggested algorithm's classification performance suggests that the 5G framework could provide an accurate, rapid, reliable, and cost-effective solution to industrial automation.

Magnetic-resonance image segmentation based on improved variable weight multi-resolution Markov random field in undecimated complex wavelet domain* *Project supported by the National Natural Science Foundation of China (Grant No. 11471004), and the Key Research and Development Program of Shaanxi Province, China (Grant No. 2018SF-251).

To solve the problem that the magnetic resonance (MR) image has weak boundaries, large amount of information, and low signal-to-noise ratio, we propose an image segmentation method based on the multi-resolution Markov random field (MRMRF) model. The algorithm uses undecimated dual-tree complex wavelet transformation to transform the image into multiple scales. The transformed low-frequency scale histogram is used to improve the initial clustering center of the K-means algorithm, and then other cluster centers are selected according to the maximum distance rule to obtain the coarse-scale segmentation. The results are then segmented by the improved MRMRF model. In order to solve the problem of fuzzy edge segmentation caused by the gray level inhomogeneity of MR image segmentation under the MRMRF model, it is proposed to introduce variable weight parameters in the segmentation process of each scale. Furthermore, the final segmentation results are optimized. We name this algorithm the variable-weight multi-resolution Markov random field (VWMRMRF). The simulation and clinical MR image segmentation verification show that the VWMRMRF algorithm has high segmentation accuracy and robustness, and can accurately and stably achieve low signal-to-noise ratio, weak boundary MR image segmentation.

Unsupervised change detection of VHR remote sensing images based on multi-resolution Markov Random Field in wavelet domain

ABSTRACT This paper proposes an unsupervised change detection method for very-high-resolution (VHR) remote sensing images based on multi-resolution Markov random field (MRF) model in wavelet domain. Firstly, the wavelet transform is performed on the difference image achieved by the change vector analysis (CVA) method, and the wavelet coefficients at each scale are obtained. Then, MRF model is constructed based on the wavelet coefficients. The wavelet high-frequency coefficients establish a feature field model that describes the feature attributes of each pixel location at each scale. The initial change map (changed and unchanged) at the coarse scale are generated through applying the k-means method to the wavelet low-frequency coefficients, and a label field model describing the region of the variation results is established. The label and feature field, at the same scale, got the optimized change map under the Bayesian criterion. Finally, the results of the low-resolution scale change map are directly projected as the adjacent higher-scale initial change map. The more accurate change map is obtained successively from the coarse scale to the original resolution scale, and the detection result of the original resolution is obtained at last. Experiments on Quick Bird, SPOT-5, and IKONOS optical images have demonstrated the effectiveness of the proposed method. The experimental results show that the method has better regional consistency and strong robustness.

Automatic measurement of yarn hairiness based on the improved MRMRF segmentation algorithm

In order to determine the yarn hairiness characteristic index more accurately, a new yarn hairiness testing strategy based on a combination of image acquisition technology is proposed. Firstly, the captured yarn images are processed with gray-scale conversion and skew correction. Secondly, yarn segmentation is implemented using a multi-resolution Markov Random Field (MRMRF) model with a variable weight in the wavelet domain and yarn stem separation is realized through iterative threshold segmentation algorithm. Thirdly, the image of yarn hairiness is extracted. Finally, the total number and actual number of yarn hairiness of different length are counted sequentially based on the segmentation lines and baseline of yarn stem edge. The baseline is obtained by calculating the average distance between yarn stem edge and yarn axis. Furthermore, the feature is analyzed. Experimental results show that, compared with visual observation method, the maximum deviation of proposed image processing algorithm is 3.88%. The proposed approach can make the results of yarn hairiness segmentation more precisely, and then the more reliable results of hairiness feature detection are obtained.

Synthetic Aperture Radar Image Recognition Using Contour Features

Synthetic aperture radar (SAR) images play an important role in nowadays target recognition. However, due to the intrinsic impact of speckle noise in SAR images, many details of the targets are confused, thus causing difficulty on target recognition. Therefore, this paper introduces a novel image recognition method which performs well on optical images. In our paper, the target is first extracted by combining the multi-resolution Markov random field method with the wavelet transform. Such a process is able to reduce the speckle noise from different resolutions. Then, morphological filtering is utilized to further reduce the remaining noise as well as smooth the boundary. With the smoothed target, we adopt the state-ofthe- art shape representation method called bag of contour fragments for target classification, which encodes the target into different contours. Then, by using support vector machine (SVM), the contours of each target can be well classified and the target recognition can be achieved. Finally, the experimental results validate the superior performance of our method on the recognition accuracy.

Fast pose invariant face recognition using super coupled multiresolution Markov Random Fields on a GPU

We discuss the problem of pose invariant face recognition using a Markov Random Field (MRF) model. MRF image to image matching has been shown to be very promising in earlier studies (Arashloo and Kittler, 2011) [4]. Its demanding computational complexity has been addressed in Arashloo et al. (2011) [6] by means of multiresolution MRFs linked by the super coupling transform advocated by Petrou et al. (1998) [37, 11]. In this paper, we benefit from the daisy descriptor for face image representation in image matching. Most importantly, we design an innovative GPU implementation of the proposed multiresolution MRF matching process. The significant speed up achieved (factor of 25) has multiple benefits: It makes the MRF approach a practical proposition. It facilitates extensive empirical optimisation and evaluation studies. The latter conducted on benchmarking databases, including the challenging labelled faces in the wild (LFW) database show the outstanding potential of the proposed method, which consistently achieves state-of-the-art performance in standard benchmarking tests. The experimental studies also show that the super coupled multiresolution MRFs deliver a computational speed up by a factor of 5 over and above the speed up achieved using the GPU implementation.

Textile Image Segmentation Using a Multi-Resolution Markov Random Field Model on Variable Weights in the Wavelet Domain

Textile Image Segmentation Using a Multi-Resolution Markov Random Field Model on Variable Weights in the Wavelet Domain

Intelligent segmentation of jacquard warp-knitted fabric using a multiresolution Markov random field with adaptive weighting in the wavelet domain

In this paper, we propose a new approach to intelligently segment jacquard warp-knitted fabric images by combining wavelet texture decomposition, multiresolution Markov random field (MRF) modeling and Bayesian parameter estimation. Firstly, we use a flat scanner to capture eight-bit grayscale images of jacquard fabrics and adopt the Gaussian low-pass filter to decrease pixel variation due to discordant light reflection arising from uneven jacquard fabric surface. To overcome the incapacity of single resolution, multiresolution wavelet texture decomposition is employed, inspired by human visual sense procedure. Next, both intra-scale and inter-scale information are taken into account by the MRF model, in which a modified feature field model of the MRF, containing spatial noise with zero-mean Gaussian distribution, is presented in light of the inherent characteristics of jacquard warp-knitted fabric image. Afterward, an adaptive weighting function is used to weaken the defect of the potential parameter set empirically during the process of parameter estimation and image segmentation. Experimental results, used to verify the performance of the proposed algorithm especially the main novelties, prove that the approach is feasible and applicable.

Multispectral textured image segmentation using a multi-resolution fuzzy Markov random field model on variable scales in the wavelet domain

This article proposes a new multispectral image texture segmentation algorithm using a multi-resolution fuzzy Markov random field model for a variable scale in the wavelet domain. The algorithm considers multi-scalar information in both vertical and lateral directions. The feature field of the scalable wavelet coefficients is modelled, combining with the fuzzy label field describing the spatially constrained correlations between neighbourhood features to achieve a more accurate parameter estimation. The extended scalable label field models the label data from different scales to obtain more homogeneous areas; image segmentation results are finally obtained according to the Bayesian rule from a coarser to a finer scale. Multispectral texture images and remote-sensing images are used to test the effectiveness of the the proposed method. Segmentation results show that the new method simultaneously presents a better performance in achieving the homogeneity of the region and accuracy of detected boundaries compared with existing image segmentation algorithms.

Texture Image Segmentation Based on MRMRF in Contourlet Domain

This paper presents a new multi-resolution Markov random field model in Contourlet domain for unsupervised texture image segmentation. In order to make full use of the merits of Contourlet transformation, we introduce the taditional MRMRF model into Contourlet domain, in a manner of variable interation between two components in the tradtional MRMRF model. Using this method, the new model can automatically estimate model parameters and produce accurate unsupervised segmentation results. The results obtained on synthetic texture images and remote sensing images demonstrate that a better segmentation is achieved by our model than the traditional MRMRF model.

Image segmentation based on multiresolution Markov random field with fuzzy constraint in wavelet domain

This study proposes a multiresolution Markov random field model with fuzzy constraint in wavelet domain (MRMRF-F). In this model, a fuzzy field is introduced into the multiresolution Markov random field model to estimate parameters, by which the spatial constraint between neighbouring features can be reflected. There are three subfields on each resolution in the MRMRF-F model: one feature field, one label field and one fuzzy field. Among these fields, a three-step iteration scheme is designed to realise image segmentation. Namely, the label field renews the fuzzy field; then the fuzzy field estimates the parameters of the feature field; and a renewed label field is obtained by maximising the products of both the probability of the feature field and the probability of current label field; image segmentation is finally obtained by the cycle iteration among these fields. Texture and natural images are used to test the performance of the new model, and experimental results illustrate a better and higher accuracy compared with other existing methods about fuzzy methods or Markov random field models.

Texture Segmentation of Jacquard Fabric Image Based on Multiresolution Markov Random Field

In order to develop an automated segmentation system for jacquard fabric images, a new approach based on MRMRF algorithm with variable weighing parameter is proposed in this paper. Firstly the variable weighting parameter different to the one in traditional MRMRF is described, which can provide a more accurate vector. The next step is MAP estimation and the model for texture segmentation. During this iterative process the initial value is big enough to learn more accurate parameters of feature energy. With the iterative number going on, the value will decrease and stop decreasing when the iterative number comes to some degree. Lastly the experiment results show that the new approach works better than the traditional method with constant weighing parameter.

Multiresolution video object extraction fitted to scalable wavelet-based object coding

To enable content based functionalities in video processing algorithms, decomposition of scenes into semantic objects is necessary. A semi-automatic Markov random field based multiresolution algorithm is presented for video object extraction in a complex scene. In the first frame, spatial segmentation and user intervention determine objects of interest. The specified objects are subsequently tracked in successive frames and newly appeared objects/regions are also detected. The video object extraction algorithm includes discrete wavelet transform decomposition multiresolution Markov random field (MRF)-based spatial segmentation with emphasis on border smoothness at different resolutions, and an MRF-based backward region classification that determines the tracked objects in the scene. Finally, a motion constraint, embedded in the region classifier, determines the newly appeared objects/regions and completes the proposed algorithm towards an efficient video segmentation algorithm. The results are applicable for generic segmentation applications, however the proposed multiresolution video segmentation algorithm supports scalable object-based wavelet coding in particular. Moreover, compared to traditional object extraction algorithms, it produces smoother and more visually pleasing shape masks at different resolutions. The proposed effective multiresolution video object extraction method allows for larger motion, better noise tolerance and less computational complexity.

A Hybrid System for Detecting Masses in Mammographic Images

This paper discusses the hybrid system for detecting masses in mammographic images. The proposed approach analyzes mammograms in three major steps. First, a global segmentation method is applied to find regions of interest. This step uses texture features, decision trees, and a multiresolution Markov random field model. The second stage works on the output of the previous algorithm. Here, a combination of three different local segmentation methods is used, and then, some relevant features are extracted. Some of them refer to the shape of the object; others are texture parameters. The final decision is made using a linear combination of these features

Detection of cancerous masses for screening mammography using discrete wavelet transform-based multiresolution Markov random field.

When cancerous masses are embedded in and camouflaged by varying densities of parenchymal tissue structures, they are difficult to visually detect on mammograms. We present a novel algorithm based on the discrete wavelet transform (DWT) and multiresolution markov random field (MMRF) that will single out the suspicious masses to assist the attending radiologist in making decisions.

Texture classification using multiresolution Markov random field models

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.

Noisy image restoration using multiresolution markov random fields

One of the basic problems in image restoration is to remove the additive noise generated by imaging systems while preserving sharp edges and details in the image. The goal of this paper is to present a restoration method based on a Markov random field (MRF) model with line-process in a multiresolution scheme. The Markov property of the intensity field takes into account local interactions between pixels, and the line process allows for the introduction of discontinuities in these interactions. Unfortunately, these models lead to nonconvex criteria. To reduce computation time, the deterministic graduated nonconvexity relaxation algorithm is used. his algorithm provides a good approximation of the global minimum. Multiresolution techniques yield a rapidly convergent algorithm and improve the visual quality of restored images. In order to adapt the model to the multiresolution scheme, a new local interaction function is used. The difficulty is to adjust parameters of the model across the resolutions. This method gives good results in terms of visual quality. Images are smoothed, while edges are preserved. We present some results on a very noisy image.