Modified Fuzzy C-Means Clustering Algorithm Research Articles

A Point Cloud Simplification Method Based on Modified Fuzzy C-Means Clustering Algorithm with Feature Information Reserved

To further improve the performance of the point cloud simplification algorithm and reserve the feature information of parts point cloud, a new method based on modified fuzzy c-means (MFCM) clustering algorithm with feature information reserved is proposed. Firstly, the normal vector, angle entropy, curvature, and density information of point cloud are calculated by combining principal component analysis (PCA) and k-nearest neighbors (k-NN) algorithm, respectively; Secondly, gravitational search algorithm (GSA) is introduced to optimize the initial cluster center of fuzzy c-means (FCM) clustering algorithm. Thirdly, the point cloud data combined coordinates with its feature information are divided by the MFCM algorithm. Finally, the point cloud is simplified according to point cloud feature information and simplified parameters. The point cloud test data are simplified using the new algorithm and traditional algorithms; then, the results are compared and discussed. The results show that the new proposed algorithm can not only effectively improve the precision of point cloud simplification but also reserve the accuracy of part features.

Extracting tidal creek features in a heterogeneous background using Sentinel-2 imagery: a case study in the Yellow River Delta, China

ABSTRACTOwing to difficulties accessing areas where tidal creeks develop and the frequent morphological changes they undergo, there are very significant advantages in using remote sensing methods to effectively monitor conditions and the dynamics in these zones. The Yellow River Delta, which is the research area of this study, exhibits a very irregular tidal flat, with tidal creeks that vary in width and experience water current anisotropy. This study proposes a method for extracting tidal creeks based on a digital image processing technology that can carry out accurate characterization of tidal creeks against heterogeneous backgrounds. First, the normalized difference water index (NDWI) and Maximum Between-Class Variance (Otsu) classification are used to delineate the wide tidal creeks. Second, the modified fuzzy c-means clustering algorithm (MFCM) is used to suppress the difference between the target and background caused by spatial heterogeneity of the tidal flat environment. Next, the Gaussian matching filter (GMF) is used to enhance the narrow tidal creeks and suppress the influence of anisotropy. Then, adaptive threshold segmentation is conducted to delineate the narrow tidal creeks. Finally, the complete tidal creek networks are delineated by combining the wide and narrow tidal creeks. The overall performance was evaluated by visual interpretation and the result is excellent. In the four test areas, the kappa coefficient (κ) was greater than 0.79, the error of Commission was less than 22%, and the error of omission was less than 15%; the proposed method performed better than the Maximum Likelihood method, Support Vector Machine, and k-means algorithms. The results show that the proposed method can differentiate tidal creeks from different research areas, with good extraction accuracy and stability. The method can provide reference data for real-time dynamic monitoring of tidal creeks and has potential for application to other tidal zones in different regions.

Extracting method of tidal creek features under heterogeneous background at Yellow River Delta using remotely sensed imagery.

The Yellow River Delta exhibits irregular tidal flat, with tidal creeks that vary in width and experience tidal creek current anisotropy. Given such characteristics, the GF-2 multi-spectral image was selected as the data source to characterize the details of tidal creeks. First, the normali-zed difference water index (NDWI) and OTSU classification were used to delineate the wide tidal creeks. Second, the modified fuzzy C-means clustering algorithm (MFCM) and multi-scale Gaussian matching filter (MGMF) were used to enhance the narrow tidal creeks on the basis of weakening the heterogeneity of tidal flat background. Then, the adaptive threshold segmentation was conducted to delineate the narrow tidal creeks. Finally, the complete tidal creek networks were delineated by combining the wide and narrow tidal creeks. We fully used the spatial resolution and spectral information of the GF-2 image and took into account the geometric features of the linear features, ensuring the spatial continuity of the tidal creek extraction results. In the four tested areas, the Kappa coefficient was greater than 0.8 and the overall accuracy was greater than 97%, which performed better than the maximum likelihood method and support vector machine. The results showed that the proposed method could completely differentiate different types of tidal creeks, with good extraction accuracy and stability. The method could provide scientific reference for real-time dynamic monitoring of tidal creek and its development and evolution.

A competent three-tier fuzzy cluster algorithm for enhanced data transmission in cluster EAACK MANETs

Security is a major issue that is being constantly addressed in infrastructure less networks. An ad hoc network like MANET faces several issues that can jeopardize the transmissions in the network. A MANET can be made manageable by clustering it and appointing a cluster head. The cluster head communicates with the other cluster heads through base station. The cluster head routes all traffic from its members with the help of the base station, both within and outside the cluster. Several schemes have been suggested to find and eliminate malicious nodes that may hinder transmission. However, when the cluster member or the cluster head turns malicious, it may damage the transmission by tampering with the data and the routing policies. Though the security schemes target the nodes, they do not have specific techniques to handle a situation where the cluster member and cluster head turn malicious. Because of the constraints prevalent in the election of the cluster head and the selection of the cluster member, an intruder can penetrate and be elected as the cluster head or a member. Our proposed approach tries to counteract this situation by using a two-phase approach. The first phase uses fuzzy rules to form clusters and the second phase uses an efficient three-tier filtering methodology to identify the reliable nodes within the network. For the clustering process, a modified fuzzy C-means clustering algorithm (MFCM) is used to form the clusters. The three-tier filtering methodology decides the authenticity of the nodes by using an efficient filtering process. Our proposed technique can assure a better performance and enhanced throughput when compared to the existing techniques of EAACK.

Automated Colorization of Grayscale Images Using Texture Descriptors and a Modified Fuzzy C-Means Clustering

A novel example-based process for Automated Colorization of grayscale images using Texture Descriptors (ACTD) without any human intervention is proposed. By analyzing a set of sample color images, coherent regions of homogeneous textures are extracted. A multi-channel filtering technique is used for texture-based image segmentation, combined with a modified Fuzzy C-means (FCM) clustering algorithm. This modified FCM clustering algorithm includes both the local spatial information from neighboring pixels, and the spatial Euclidian distance to the cluster’s center of gravity. For each area of interest, state-of-the-art texture descriptors are then computed and stored, along with corresponding color information. These texture descriptors and the color information are used for colorization of a grayscale image with similar textures. Given a grayscale image to be colorized, the segmentation and feature extraction processes are repeated. The texture descriptors are used to perform Content-Based Image Retrieval (CBIR). The colorization process is performed by Chroma replacement. This research finds numerous applications, ranging from classic film restoration and enhancement, to adding valuable information into medical and satellite imaging. Also, this can be used to enhance the detection of objects from x-ray images at the airports.