Surface Reconstruction Using Adaptive Clustering Methods

Purpose – The performance of three frequently used level set-based segmentation methods is examined for the purpose of defining features and boundary conditions for image-based Eulerian fluid and solid mechanics models. The focus of the evaluation is to identify an approach that produces the best geometric representation from a computational fluid/solid modeling point of view. In particular, extraction of geometries from a wide variety of imaging modalities and noise intensities, to supply to an immersed boundary approach, is targeted. Design/methodology/approach – Two- and three-dimensional images, acquired from optical, X-ray CT, and ultrasound imaging modalities, are segmented with active contours, k-means, and adaptive clustering methods. Segmentation contours are converted to level sets and smoothed as necessary for use in fluid/solid simulations. Results produced by the three approaches are compared visually and with contrast ratio, signal-to-noise ratio, and contrast-to-noise ratio measures. Findings – While the active contours method possesses built-in smoothing and regularization and produces continuous contours, the clustering methods (k-means and adaptive clustering) produce discrete (pixelated) contours that require smoothing using speckle-reducing anisotropic diffusion (SRAD). Thus, for images with high contrast and low to moderate noise, active contours are generally preferable. However, adaptive clustering is found to be far superior to the other two methods for images possessing high levels of noise and global intensity variations, due to its more sophisticated use of local pixel/voxel intensity statistics. Originality/value – It is often difficult to know a priori which segmentation will perform best for a given image type, particularly when geometric modeling is the ultimate goal. This work offers insight to the algorithm selection process, as well as outlining a practical framework for generating useful geometric surfaces in an Eulerian setting.

Download Full-text

Implicit Surface Reconstruction Based on Adaptive Clustering

2011 12th International Conference on Computer-Aided Design and Computer Graphics ◽

10.1109/cad/graphics.2011.64 ◽

2011 ◽

Author(s):

Chao Zheng ◽

Hui Zhang

Keyword(s):

Surface Reconstruction ◽

Implicit Surface ◽

Adaptive Clustering

Download Full-text

A New Adaptive Elastic Net Method for Cluster Analysis

International Journal of Computers Communications & Control ◽

10.15837/ijccc.2017.3.2796 ◽

2017 ◽

Vol 12 (3) ◽

pp. 429

Author(s):

Junyan Yi ◽

Peixin Zhao ◽

Lei Zhang ◽

Gang Yang

Keyword(s):

Cluster Analysis ◽

Local Search ◽

Nearest Neighbor ◽

Dimensional Space ◽

Cyclic Permutation ◽

Research Problem ◽

Elastic Net ◽

Clustering Methods ◽

Partitional Clustering ◽

Adaptive Clustering

Clustering is inherently a highly challenging research problem. The elastic net algorithm is designed to solve the traveling salesman problem initially, now is verified to be an efficient tool for data clustering in n-dimensional space. In this paper, by introducing a nearest neighbor learning method and a local search preferred strategy, we proposed a new Self-Organizing NN approach, called the Adaptive Clustering Elastic Net (ACEN) to solve the cluster analysis problems. ACEN consists of the adaptive clustering elastic net phase and a local search preferred phase. The first phase is used to find a cyclic permutation of the points as to minimize the total distances of the adjacent points, and adopts the Euclidean distance as the criteria to assign each point. The local search preferred phase aims to minimize the total dissimilarity within each clusters. Simulations were made on a large number of homogeneous and nonhomogeneous artificial clusters in n dimensions and a set of publicly standard problems available from UCI. Simulation results show that compared with classical partitional clustering methods, ACEN can provide better clustering solutions and do more efficiently.

Download Full-text

Representation Learning Based on Autoencoder and Deep Adaptive Clustering for Image Clustering

Mathematical Problems in Engineering ◽

10.1155/2021/3742536 ◽

2021 ◽

Vol 2021 ◽

pp. 1-11

Author(s):

Siquan Yu ◽

Jiaxin Liu ◽

Zhi Han ◽

Yong Li ◽

Yandong Tang ◽

...

Keyword(s):

Local Structure ◽

Image Representation ◽

Representation Learning ◽

Classification Problem ◽

Image Clustering ◽

Clustering Methods ◽

Adaptive Clustering ◽

Complex Procedure ◽

Convolutional Autoencoder ◽

Image Representations

Image clustering is a complex procedure, which is significantly affected by the choice of image representation. Most of the existing image clustering methods treat representation learning and clustering separately, which usually bring two problems. On the one hand, image representations are difficult to select and the learned representations are not suitable for clustering. On the other hand, they inevitably involve some clustering step, which may bring some error and hurt the clustering results. To tackle these problems, we present a new clustering method that efficiently builds an image representation and precisely discovers cluster assignments. For this purpose, the image clustering task is regarded as a binary pairwise classification problem with local structure preservation. Specifically, we propose here such an approach for image clustering based on a fully convolutional autoencoder and deep adaptive clustering (DAC). To extract the essential representation and maintain the local structure, a fully convolutional autoencoder is applied. To manipulate feature to clustering space and obtain a suitable image representation, the DAC algorithm participates in the training of autoencoder. Our method can learn an image representation that is suitable for clustering and discover the precise clustering label for each image. A series of real-world image clustering experiments verify the effectiveness of the proposed algorithm.

Download Full-text

An Affinity Propagation-Based Self-Adaptive Clustering Method for Wireless Sensor Networks

Sensors ◽

10.3390/s19112579 ◽

2019 ◽

Vol 19 (11) ◽

pp. 2579 ◽

Cited By ~ 44

Author(s):

Jin Wang ◽

Yu Gao ◽

Kai Wang ◽

Arun Kumar Sangaiah ◽

Se-Jung Lim

Keyword(s):

Machine Learning ◽

Information Exchange ◽

Network Performance ◽

Learning Algorithm ◽

Affinity Propagation ◽

Wireless Sensor ◽

Clustering Methods ◽

Clustering Method ◽

Adaptive Clustering ◽

Self Adaptive

A wireless sensor network (WSN) is an essential component of the Internet of Things (IoTs) for information exchange and communication between ubiquitous smart objects. Clustering techniques are widely applied to improve network performance during the routing phase for WSN. However, existing clustering methods still have some drawbacks such as uneven distribution of cluster heads (CH) and unbalanced energy consumption. Recently, much attention has been paid to intelligent clustering methods based on machine learning to solve the above issues. In this paper, an affinity propagation-based self-adaptive (APSA) clustering method is presented. The advantage of K-medoids, which is a traditional machine learning algorithm, is combined with the affinity propagation (AP) method to achieve more reasonable clustering performance. AP is firstly utilized to determine the number of CHs and to search for the optimal initial cluster centers for K-medoids. Then the modified K-medoids is utilized to form the topology of the network by iteration. The presented method effectively avoids the weakness of the traditional K-medoids in aspects of the homogeneous clustering and convergence rate. Simulation results show that the proposed algorithm outperforms some latest work such as the unequal cluster-based routing scheme for multi-level heterogeneous WSN (UCR-H), the low-energy adaptive clustering hierarchy using affinity propagation (LEACH-AP) algorithm, and the energy degree distance unequal clustering (EDDUCA) algorithm.

Download Full-text

Computational Intelligence based Clustering Algorithms for Wireless Sensor Networks: Trends and Possible Solutions

Turkish Journal of Computer and Mathematics Education (TURCOMAT) ◽

10.17762/turcomat.v12i2.2339 ◽

2021 ◽

Vol 12 (2) ◽

pp. 2992-3002

Author(s):

Nitin Mittal

Keyword(s):

Computational Intelligence ◽

Clustering Algorithms ◽

Cluster Head ◽

Low Energy ◽

Wireless Sensor ◽

Clustering Methods ◽

Radio Communication ◽

Adaptive Clustering ◽

Ch Selection ◽

Selection Of

A wireless sensor network (WSN) is a state-of-the-art technology for radio communication. A WSN includes several sensors that are arbitrarily distributed in a particular region to detect and track physical characteristics that are hard for humans to observe, like temperature, humidity, and pressure. Because of the nature of WSNs, many issues may arise, including information routing, power consumption, clustering, and cluster head (CH) selection. Although there are still some difficulties in the WSN, owing to its versatility and robustness, it has gained considerable attention among scientists and technologists despite the shortcomings. Various protocols were designed to solve these problems. Low energy adaptive clustering hierarchy (LEACH) is one of the significant hierarchical protocols used to reduce energy consumption in WSNs. This article provides an extensive analysis of LEACH-variant clustering protocols for WSNs. Recent research on Machine Learning, Computational Intelligence, and WSNs has highlighted the optimized WSN clustering algorithms. However, the selection of a suitable paradigm for a clustering solution continues an issue owing to the diversity of WSN applications. In this paper, a comprehensive review of suggested optimized clustering alternatives has been conducted and a comparison of these optimized clustering methods has been suggested based on various performance parameters. The centralized clustering approaches based on the Swarm Intelligence paradigm are observed to be more suitable for the applications in which low energy is required, high information delivery rate, or elevated scalability than algorithms that are based on the other paradigms described.

Download Full-text