Shell Clustering Research Articles

Core–shell clustering approach for detection and analysis of coastal upwelling

A comprehensive approach is presented to analyze season’s coastal upwelling represented by weekly sea surface temperature (SST) image grids. The proposed model, core–shell clustering, assumes that the season’s upwelling can be divided into shorter periods of stability, time ranges, consisting of constant core and variable shell parts. A one-by-one core–shell clustering algorithm is provided. The algorithm parameters are automatically derived from the least-squares clustering criterion.The approach applies to SST gridded data for sixteen successive years (2004–2019) of coastal upwelling in the western Iberian coast, the northernmost branch of the Canary Current Upwelling System. Our results show that at each season, there are 3 to 5 time intervals, the ranges, at which the upwelling presents stable core patterns of relatively cold water surrounded by somewhat larger shell areas of warmer waters. Based on other experimental computations performed by our team, we conclude that this pattern is not just a purely local phenomenon but has a more global meaning. Inter-annual time series analysis are consistent among themselves and with existing expert domain knowledge.

Probing ab initio emergence of nuclear rotation

Structural phenomena in nuclei, from shell structure and clustering to superfluidity and collective rotations and vibrations, reflect emergent degrees of freedom. Ab initio theory describes nuclei directly from a fully microscopic formulation. We can therefore look to ab initio theory as a means of exploring the emergence of effective degrees of freedom in nuclei. For the illustrative case of emergent rotational bands in the Be isotopes, we establish an understanding of the underlying oscillator space and angular momentum (orbital and spin) structure. We consider no-core configuration interaction (NCCI) calculations for 7,9,11Be with the Daejeon16 internucleon interaction. Although shell model or rotational degrees of freedom are not assumed in the ab initio theory, the NCCI results are suggestive of the emergence of effective shell model degrees of freedom (0 hbar-omega and 2 hbar-omega excitations) and LS-scheme rotational degrees of freedom, consistent with an Elliott-Wilsdon SU(3) description. These results provide some basic insight into the connection between emergent effective collective rotational and shell model degrees of freedom in these light nuclei and the underlying ab initio microscopic description.

A generalized possibilistic approach to shell clustering of template-based shapes

ABSTRACTEfficiently and effectively detecting shell-like structures of particular shapes is an important task in computer vision and image processing. This paper presents a generalized possibilistic c-means algorithm (PCM) for shell clustering based on the diversity index of degree-λ proposed by Patil and Taillie [Diversity as a concept and its measurement. J Amer Statist Assoc. 1982;77:548–561]. Experiments on various data sets in Wang [Possibilistic shell clustering of template-based shapes. IEEE Trans Fuzzy Syst. 2009;17:777–793] show that the the proposed generalized PCM performs better than Wang's [Possibilistic shell clustering of template-based shapes. IEEE Trans Fuzzy Syst. 2009;17:777–793] possibilistic shell clustering method according two two criteria: (i) the ‘grade of detection’ for each target cluster; (ii) the amount of computation, denoted as , required to attain a given .

Robust Spherical Shell Clustering Using Fuzzy-Possibilistic Product Partition

One of the main challenges in the field of clustering is creating algorithms that are both accurate and robust. This paper introduces a novel fuzzy-possibilistic shell clustering model aiming at accurate detection of circles, spheres, and multidimensional spheroids in the presence of outlier data. The proposed fuzzy-possibilistic product partition c-spherical shell algorithm (FP3CSS) combines the probabilistic and possibilistic partitions in a qualitatively different way from previous, similar algorithms. The novel mixture partition is able to suppress the influence of extreme outlier data, which gives it net superiority in terms of robustness and accuracy, compared to previous algorithms.

Quantum Isomorphic Shell Model: Multi-Harmonic Shell Clustering of Nuclei

The present multi-harmonic shell clustering of a nucleus is a direct consequence of the fermionic nature of nucleons and their average sizes. The most probable form and the average size for each proton or neutron shell are here presented by a specific equilibrium polyhedron of definite size. All such polyhedral shells are closely packed leading to a shell clustering of a nucleus. A harmonic oscillator potential is employed for each shell. All magic and semi-magic numbers, g.s. single particle and total binding energies, proton, neutron and mass radii of 40Ca, 48Ca, 54Fe, 90Zr, 108Sn, 114Te, 142Nd, and 208Pb are very successfully predicted.

Template-Based Shell Clustering Using a Line-Segment Representation of Data

This paper presents the algorithms and experimental results for template-based shell clustering when the datasets are represented by line segments. Compared with point datasets, such representations have several advantages, which include better scalability and noise immunity, as well as the availability of orientation information. Using both synthetic and real-world image datasets, we have experimentally demonstrated that line-segment-based representations result in both better accuracy and better efficiency in shell clustering.

Model-based fuzzy c-shells clustering

In this paper, a new shell clustering method is presented to cluster model-based shells in 2-dimensions. Shells, that each one of them can be expressed by a center and non-negative radius in each angle (by considering polar coordinate system), can cluster by using the proposed model-based fuzzy c-shells (MFCS) clustering method. In this paper, firstly one of the most famous traditionally clustering methods, i.e. fuzzy c-spherical shells (FCSS) clustering method, is extracted from the proposed MFCS clustering method as a specific state of it. Then, the performance of proposed method is examined in three examples when it is applied over shells with various shapes in 2-dimensions. Since the resulted systems of equations in the studied examples cannot be solved directly, the particle swarm optimization (PSO) algorithm is used to numerically solve the resulted equations systems. The simulation results show the acceptable performance of the proposed MFCS method.

Possibilistic Shell Clustering of Template-Based Shapes

In this paper, we present a new type of alternating-optimization-based possibilistic c-shell algorithm for clustering-template-based shapes. A cluster prototype consists of a copy of the template after translation, scaling, rotation, and/or affine transformations. This extends the capability of shell clustering beyond a few standard geometrical shapes that have been in the literature so far. We use a number of 2-D datasets, consisting of both synthetic and real-world images, to illustrate the capability of our algorithm in detecting generic-template-based shapes in images. We also describe a progressive clustering procedure aimed to relax the requirements for a known number of clusters and good initialization, as well as new performance measures of shell-clustering algorithms.

Detecting and measuring rings in banknote images

Various intelligent systems show a rapidly growing potential use of visual information processing technologies. This paper presents an example of employing visual information processing technologies for detecting and measuring rings in banknote images. The size of the rings is one of parameters used to inspect the banknote printing quality. The approach developed consists of two phases. In the first phase, based on histogram processing and data clustering, image areas containing rings are localized and edges of the rings are detected. Then, in the second phase, applying the hard and possibilistic spherical shell clustering to the extracted edge pixels the ring centre and radii are estimated. The experimental investigations performed have shown that even highly occluded rings are robustly detected. Several prototypes of the system developed have been installed in two banknote printing shops in Europe.

FCV法の一般化と最小絶対誤差に基づくロバストなシェルクラスタリング

データが非線形な特異性を持っている場合への主成分分析の応用として, データの布置に最もよく合う非線形座標系を求める一般化主成分分析法が提案されている. 一方, 曲面や球面上に分布したデータの局所的な構造を抽出するシェルクラスタリング法は, 画像認識などへの応用が研究されている. 本研究では, メンバシップを考慮した一般化主成分分析法に最小絶対誤差に基づく定式化を導入することにより, ロバストなジェルクラスタリング法を提案する. 拡張次元におけるマイナー成分分析とFCM法の融合手法である提案法は, 線形クラスタリング法であるBezdekらのFuzzy c-Varieties (FCV)法のアルゴリズムを非線形構造の抽出に応用したものであり, 線形多様体の抽出と球状のクラスタリングの優先度を決めるトレードオフパラメータを変化させることにより, 初期分割に依存しないクラスタリング結果が得られる. 数値実験では, 従来の可能性的制約を用いたジェルクラスタリング法との比較を通して,最小絶対誤差に基づく手法の有効性を示す.

A fuzzy shell clustering approach to recognize hyperbolic signatures in subsurface radar images

The authors propose a pattern recognition approach to analyze subsurface radar images and recognize the hyperbolic signatures produced by targets. After enhancing these signatures using a wavelet denoising procedure, pixels are grouped into significant hyperbolic shapes by fuzzy clustering. The approach also provides a validation measure for each recognized shape using so-called "shell thickness". Excellent experimental results justify the use of this algorithm for automatic interpretation of subsurface radar images.

Transport of light ions in matter

A recent set of light ion experiments are analyzed using the Green's function method of solving the Boltzmann equation for ions of high charge and energy (the GRNTRN transport code) and the NUCFRG2 fragmentation database generator code. Although the NUCFRG2 code reasonably represents the fragmentation of heavy ions, the effects of light ion fragmentation requires a more detailed nuclear model including shell structure and short range correlations appearing as tightly bound clusters in the light ion nucleus. The most recent NUCFRG2 code is augmented with a quasielastic alpha knockout model and semiempirical adjustments (up to 30 percent in charge removal) in the fragmentation process allowing reasonable agreement with the experiments to be obtained. A final resolution of the appropriate cross sections must await the full development of a coupled channel reaction model in which shell structure and clustering can be accurately evaluated.

Fuzzy shell clustering algorithms in image processing: fuzzy C-rectangular and 2-rectangular shells

Objective function-based clustering has been generalized recently to detect contours of circles and ellipses or even hyperbolas in a set of binary data vectors. Although there are special algorithms to discover lines, the detection of rectangles needs further treatment. A simple line-detection algorithm is not sufficient for rectangles since for identifying four lines as one rectangle, additional information such as the length of the lines and whether they are parallel or meet at a right angle is necessary. In this paper, a special fuzzy shell-clustering algorithm for rectangular contours is developed. The principal idea behind it can be generalized for other polygons so we also derive an algorithm that is capable of detecting rectangles and other polygons as well as approximating circles, ellipses, and lines.

Qualitative primitive identification using fuzzy clustering and invariant approach

This paper addresses the issue of qualitative primitive extraction from range images. The fuzzy C-shell clustering technique is applied to partition range images into a set of quadric shells. Once the best-fitting quadric shells have been recovered, five types of invariants are used to classify the qualitative shapes and represent the geometric parameters of the shapes. Finally, the extracted quadric shells are localised in terms of quadric centre(s), quadric directions, and any axes of revolution. Using fuzzy shell clustering, the shell features can be segmented and fitted simultaneously, and individual best-fitted shells can be clustered concurrently. The integration of the partition with the invariant analysis makes it possible to identify qualitative features from depth maps.

Validating fuzzy partitions obtained through c-shells clustering

Validation of fuzzy partitions induced through c-shells clustering is considered. The classical validity measures based on fuzzy partition alone are shown to be inadequate in capturing the shell sub-structure imposed by the shell clustering algorithm. Therefore, performance measures specifically designed for c-shells clustering are considered. Through examples, the new set of indices are shown to be capable of validating the structure characterized by the shell clustering algorithms. The issues related to classical cluster validity versus individual cluster validity are also discussed.

Origin of Concretion-Hosted Shell Clusters in the Late Cretaceous Bearpaw Formation, Southern Alberta, Canada

The widespread phenomenon of faunal clustering in concretions is examined in examples from the Late Cretaceous Bearpaw Formation of southern Alberta. Concretion-hosted shell clusters have traditionally been interpreted as biologically formed features. In contrast, sedimentologic, taphonomic and faunal evidence in the Bearpaw Formation suggests that shell clustering is inherently related to a common physical control. The clustered nature of the Bearpaw assemblages reflects the accumulation of shells in isolated storm scours. Differences in faunal composition resulted from variations in (1) the mode of scour initiation, (2) the faunal content of sea-floor sediments, and (3) the degree of shell transport experienced during storms. The formation of the isolated scours is attributed to locally augmented erosion in the vicinity of bed defects and objects exposed on the sea floor. The common restriction of fossils to concretions reflects preferred concretion growth around the shell-rich scour fills. Previously described examples of concretion-hosted shell clusters are re-interpreted in light of the scour-fill hypothesis.

Fuzzy and possibilistic shell clustering algorithms and their application to boundary detection and surface approximation. I

Traditionally, prototype-based fuzzy clustering algorithms such as the Fuzzy C Means (FCM) algorithm have been used to find "compact" or "filled" clusters. Recently, there have been attempts to generalize such algorithms to the case of hollow or "shell-like" clusters, i.e., clusters that lie in subspaces of feature space. The shell clustering approach provides a powerful means to solve the hitherto unsolved problem of simultaneously fitting multiple curves/surfaces to unsegmented, scattered and sparse data. In this paper, we present several fuzzy and possibilistic algorithms to detect linear and quadric shell clusters. We also introduce generalizations of these algorithms in which the prototypes represent sets of higher-order polynomial functions. The suggested algorithms provide a good trade-off between computational complexity and performance, since the objective function used in these algorithms is the sum of squared distances, and the clustering is sensitive to noise and outliers. We show that by using a possibilistic approach to clustering, one can make the proposed algorithms robust.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

Fuzzy and possibilistic shell clustering algorithms and their application to boundary detection and surface approximation. II

Fuzzy and possibilistic shell clustering algorithms and their application to boundary detection and surface approximation. II

The Fuzzy C Quadric Shell clustering algorithm and the detection of second-degree curves

This paper introduces a new fuzzy clustering algorithm called the Fuzzy C Quadric Shells algorithm which is expressly designed to seek clusters that can be described by segments of second-degree curves, or more generally by segments of shells of hyperquadrics.