8,331 research outputs found

    Fuzzy Clustering for Image Segmentation Using Generic Shape Information

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    The performance of clustering algorithms for image segmentation are highly sensitive to the features used and types of objects in the image, which ultimately limits their generalization capability. This provides strong motivation to investigate integrating shape information into the clustering framework to improve the generality of these algorithms. Existing shape-based clustering techniques mainly focus on circular and elliptical clusters and so are unable to segment arbitrarily-shaped objects. To address this limitation, this paper presents a new shape-based algorithm called fuzzy clustering for image segmentation using generic shape information (FCGS), which exploits the B-spline representation of an object's shape in combination with the Gustafson-Kessel clustering algorithm. Qualitative and quantitative results for FCGS confirm its superior segmentation performance consistently compared to well-established shape-based clustering techniques, for a wide range of test images comprising various regular and arbitrary-shaped objects

    Implementation of some cluster validity methods for fuzzy cluster analysis

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    Cluster analysis is an important tool in the exploration of large collections of data, revealing patterns and significant correlations in the data. The fuzzy approach to the clustering problem enhances the modeling capability as the results are expressed in soft clusters (instead of crisp clusters), where the data points may have partial memberships in several clusters. In this paper we will discuss about the most used fuzzy cluster analysis techniques and we will address an important issue: finding the optimal number of clusters. This problem is known as the cluster validity problem and is one of the most challenging aspects of fuzzy and classical cluster analysis. We will describe several methods and we will combine and compare them on several synthetic data sets

    Similarity-Driven Cluster Merging Method for Unsupervised Fuzzy Clustering

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    In this paper, a similarity-driven cluster merging method is proposed for unsupervised fuzzy clustering. The cluster merging method is used to resolve the problem of cluster validation. Starting with an overspecified number of clusters in the data, pairs of similar clusters are merged based on the proposed similarity-driven cluster merging criterion. The similarity between clusters is calculated by a fuzzy cluster similarity matrix, while an adaptive threshold is used for merging. In addition, a modified generalized objective function is used for prototype-based fuzzy clustering. The function includes the p-norm distance measure as well as principal components of the clusters. The number of the principal components is determined automatically from the data being clustered. The performance of this unsupervised fuzzy clustering algorithm is evaluated by several experiments of an artificial data set and a gene expression data set.Singapore-MIT Alliance (SMA

    The effect of low number of points in clustering validation via the negentropy increment

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    This is the author’s version of a work that was accepted for publication in Neurocomputing. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Neurocomputing, 74, 16, (2011) DOI: 10.1016/j.neucom.2011.03.023Selected papers of the 10th International Work-Conference on Artificial Neural Networks (IWANN2009)We recently introduced the negentropy increment, a validity index for crisp clustering that quantifies the average normality of the clustering partitions using the negentropy. This index can satisfactorily deal with clusters with heterogeneous orientations, scales and densities. One of the main advantages of the index is the simplicity of its calculation, which only requires the computation of the log-determinants of the covariance matrices and the prior probabilities of each cluster. The negentropy increment provides validation results which are in general better than those from other classic cluster validity indices. However, when the number of data points in a partition region is small, the quality in the estimation of the log-determinant of the covariance matrix can be very poor. This affects the proper quantification of the index and therefore the quality of the clustering, so additional requirements such as limitations on the minimum number of points in each region are needed. Although this kind of constraints can provide good results, they need to be adjusted depending on parameters such as the dimension of the data space. In this article we investigate how the estimation of the negentropy increment of a clustering partition is affected by the presence of regions with small number of points. We find that the error in this estimation depends on the number of points in each region, but not on the scale or orientation of their distribution, and show how to correct this error in order to obtain an unbiased estimator of the negentropy increment. We also quantify the amount of uncertainty in the estimation. As we show, both for 2D synthetic problems and multidimensional real benchmark problems, these results can be used to validate clustering partitions with a substantial improvement.This work has been funded by DGUI-CAM/UAM (Project CCG10-UAM/TIC-5864

    라이다 점군자료를 이용한 암반 불연속면 특성검출 자동화 연구

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    학위논문 (박사) -- 서울대학교 대학원 : 공과대학 에너지시스템공학부, 2020. 8. 전석원.The technique for determining rock mass quality and its stability is an important issue often encountered in many engineering projects including open pit and underground mines, slopes, tunnels, dams and others. Hand-mapping has been widely used as a conventional way to collect information of rock mass and determine the rock mass class. Then, a quick, safe and objective way for assessment of rock mass quality is desired to maximize the efficiency and economic benefits of the task as well as to provide essential feedback for the design, construction and operation of engineering projects. In this study, a light detection and ranging (LiDAR) technique, which can acquire 3D point cloud information quickly and accurately, was used to compensate for the shortcomings of field geological hand-mapping methods (scan line survey, window mapping survey, etc.). The geological strength index (GSI) was assessed by quantifying the characteristics of rock discontinuity using the point cloud data obtained from LiDAR scan on rock slopes. A circular window was adopted to visually represent the distribution of rock mass quality in a target rock mass. Prior to rock discontinuity characterization using LiDAR, the most important step is to extract the discontinuities from the point cloud. Thus, a triangulated irregular network was constructed using the ball-pivoting algorithm. Then, a patch was extracted by defining a set of triangular elements that satisfies the angle condition between adjacent triangular elements as a patch. Patch detection performance according to the different conditions of angle and point interval was confirmed to be independently applicable to the density of different point clouds, based on the specification or measurement location and distance of the LIDAR equipment. Optimal conditions were applied for determining the orientation of the joint, smoothness, waviness, joint spacing, and block volume. The results showed a good agreement among these factors, and thus, could be applied to two sites for comparison of measurements by the LiDAR process and hand-mapping. Consequently, similar GSI values were obtained, confirming the applicability of GSI rock classification using LiDAR. After a GSI calculation employing an overlapping circular window, a technique for determining the GSI distribution was presented using the contour plot shown in the point cloud for the target. This study aims to develop an automated algorithm that can minimize the the human bias and risk associated with field work, to quickly calculate the GSI with less manpower, and to be applied to sites requiring rapid rock engineering decisions. Another consideration is the reduction of labor and time consumed in hand-mapping. Such advantages can be maximized especially in huge survey areas or areas inaccessible targets.1. Introduction 1 1.1. Motivation 1 1.2. Research scope and contents 4 2. Background 5 2.1. Engineering rock mass classification 5 2.2. Acquisition of spatial data using LiDAR 16 3. Assessment of rock mass classification index using LiDAR 22 3.1. Joint orientation 22 3.1.1. Patch extraction method 22 3.1.2. Joint set clustering 26 3.2. Smoothness 32 3.2.1. Roughness parameter 32 3.2.2. Regression equation for roughness calculation 38 3.3. Waviness 53 3.4. Spacing 55 3.5. Block volume 59 3.6. Assessment of GSI 61 4. Application and validation 71 4.1. Mountain Gwanak (Site 1) 71 4.1.1. Field overview and LiDAR scanning at Site 1 71 4.1.2. Rock mass characterization using LiDAR at Site 1 74 4.1.3. Assessment of GSI at Site 1 115 4.2. Bangudae site (Site 2) 122 4.2.1. Field overview and LiDAR scanning at Site 2 123 4.2.2. Rock mass characterization using LiDAR at Site 2 127 4.2.3. Assessment of GSI at Site 2 163 4.3. Summary 174 5. Conclusions 178 References 181 국 문 초 록 191Docto

    Normality-based validation for crisp clustering

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    This is the author’s version of a work that was accepted for publication in Pattern Recognition. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Pattern Recognition, 43, 36, (2010) DOI 10.1016/j.patcog.2009.09.018We introduce a new validity index for crisp clustering that is based on the average normality of the clusters. Unlike methods based on inter-cluster and intra-cluster distances, this index emphasizes the cluster shape by using a high order characterization of its probability distribution. The normality of a cluster is characterized by its negentropy, a standard measure of the distance to normality which evaluates the difference between the cluster's entropy and the entropy of a normal distribution with the same covariance matrix. The definition of the negentropy involves the distribution's differential entropy. However, we show that it is possible to avoid its explicit computation by considering only negentropy increments with respect to the initial data distribution, where all the points are assumed to belong to the same cluster. The resulting negentropy increment validity index only requires the computation of covariance matrices. We have applied the new index to an extensive set of artificial and real problems where it provides, in general, better results than other indices, both with respect to the prediction of the correct number of clusters and to the similarity among the real clusters and those inferred.This work has been partially supported with funds from MEC BFU2006-07902/BFI, CAM S-SEM-0255-2006 and CAM/UAM CCG08-UAM/TIC-442

    Evidential Evolving Gustafson-Kessel Algorithm For Online Data Streams Partitioning Using Belief Function Theory.

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    International audienceA new online clustering method called E2GK (Evidential Evolving Gustafson-Kessel) is introduced. This partitional clustering algorithm is based on the concept of credal partition defined in the theoretical framework of belief functions. A credal partition is derived online by applying an algorithm resulting from the adaptation of the Evolving Gustafson-Kessel (EGK) algorithm. Online partitioning of data streams is then possible with a meaningful interpretation of the data structure. A comparative study with the original online procedure shows that E2GK outperforms EGK on different entry data sets. To show the performance of E2GK, several experiments have been conducted on synthetic data sets as well as on data collected from a real application problem. A study of parameters' sensitivity is also carried out and solutions are proposed to limit complexity issues

    Statistical Traffic State Analysis in Large-scale Transportation Networks Using Locality-Preserving Non-negative Matrix Factorization

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    Statistical traffic data analysis is a hot topic in traffic management and control. In this field, current research progresses focus on analyzing traffic flows of individual links or local regions in a transportation network. Less attention are paid to the global view of traffic states over the entire network, which is important for modeling large-scale traffic scenes. Our aim is precisely to propose a new methodology for extracting spatio-temporal traffic patterns, ultimately for modeling large-scale traffic dynamics, and long-term traffic forecasting. We attack this issue by utilizing Locality-Preserving Non-negative Matrix Factorization (LPNMF) to derive low-dimensional representation of network-level traffic states. Clustering is performed on the compact LPNMF projections to unveil typical spatial patterns and temporal dynamics of network-level traffic states. We have tested the proposed method on simulated traffic data generated for a large-scale road network, and reported experimental results validate the ability of our approach for extracting meaningful large-scale space-time traffic patterns. Furthermore, the derived clustering results provide an intuitive understanding of spatial-temporal characteristics of traffic flows in the large-scale network, and a basis for potential long-term forecasting.Comment: IET Intelligent Transport Systems (2013

    A Self-Organizing System for Classifying Complex Images: Natural Textures and Synthetic Aperture Radar

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    A self-organizing architecture is developed for image region classification. The system consists of a preprocessor that utilizes multi-scale filtering, competition, cooperation, and diffusion to compute a vector of image boundary and surface properties, notably texture and brightness properties. This vector inputs to a system that incrementally learns noisy multidimensional mappings and their probabilities. The architecture is applied to difficult real-world image classification problems, including classification of synthetic aperture radar and natural texture images, and outperforms a recent state-of-the-art system at classifying natural texturns.Office of Naval Research (N00014-95-1-0409, N00014-95-1-0657, N00014-91-J-4100); Advanced Research Projects Agency (N00014-92-J-4015); Air Force Office of Scientific Research (F49620-92-J-0225, F49620-92-J-0334); National Science Foundation (IRI-90-00530, IRI-90-24877
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