1,777 research outputs found
Fuzzy Clustering for Image Segmentation Using Generic Shape Information
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
Robust approach to object recognition through fuzzy clustering and hough transform based methods
Object detection from two dimensional intensity images as well as three dimensional range images is considered. The emphasis is on the robust detection of shapes such as cylinders, spheres, cones, and planar surfaces, typically found in mechanical and manufacturing engineering applications. Based on the analyses of different HT methods, a novel method, called the Fast Randomized Hough Transform (FRHT) is proposed. The key idea of FRHT is to divide the original image into multiple regions and apply random sampling method to map data points in the image space into the parameter space or feature space, then obtain the parameters of true clusters. This results in the following characteristics, which are highly desirable in any method: high computation speed, low memory requirement, high result resolution and infinite parameter space. This project also considers use of fuzzy clustering techniques, such as Fuzzy C Quadric Shells (FCQS) clustering algorithm but combines the concept of noise prototype to form the Noise FCQS clustering algorithm that is robust against noise. Then a novel integrated clustering algorithm combining the advantages of FRHT and NFCQS methods is proposed. It is shown to be a robust clustering algorithm having the distinct advantages such as: the number of clusters need not be known in advance, the results are initialization independent, the detection accuracy is greatly improved, and the computation speed is very fast. Recent concepts from robust statistics, such as least trimmed squares estimation (LTS), minimum volume ellipsoid estimator (MVE) and the generalized MVE are also utilized to form a new robust algorithm called the generalized LTS for Quadric Surfaces (GLTS-QS) algorithm is developed. The experimental results indicate that the clustering method combining the FRHT and the GLTS-QS can improve clustering performance. Moreover, a new cluster validity method for circular clusters is proposed by considering the distribution of the points on the circular edge. Different methods for the computation of distance of a point from a cluster boundary, a common issue in all the range image clustering algorithms, are also discussed. The performance of all these algorithms is tested using various real and synthetic range and intensity images. The application of the robust clustering methods to the experimental granular flow research is also included
A hybrid hair model using three dimensional fuzzy textures
Cataloged from PDF version of article.Human hair modeling and rendering have always been a challenging topic in
computer graphics. The techniques for human hair modeling consist of explicit
geometric models as well as volume density models. Recently, hybrid cluster
models have also been successful in this subject. In this study, we present a
novel three dimensional texture model called 3D Fuzzy Textures and algorithms
to generate them. Then, we use the developed model along with a cluster model
to give human hair complex hairstyles such as curly and wavy styles. Our model
requires little user effort to model curly and wavy hair styles. With this study,
we aim at eliminating the drawbacks of the volume density model and the cluster
hair model with 3D fuzzy textures. A three dimensional cylindrical texture mapping
function is introduced for mapping purposes. Current generation graphics
hardware is utilized in the design of rendering system enabling high performance
rendering.Aran, Medeni ErolM.S
Piece‐wise constant cluster modelling of dynamics of upwelling patterns
A comprehensive approach is presented to analyse season's coastal upwelling represented by weekly sea surface temperature (SST) image grids. Our three-stage data recovery clustering method assumes that the season's upwelling can be divided into shorter periods of stability, ranges, each to be represented by a constant core and variable shell parts. Corresponding clustering algorithms parameters are automatically derived by using the least-squares clustering criterion. The approach has been successfully applied to real-world SST data covering two distinct regions: Portuguese coast and Morocco coast, for 16 years each.LA/P/0101/2020info:eu-repo/semantics/publishedVersio
Acoustic data optimisation for seabed mapping with visual and computational data mining
Oceans cover 70% of Earth’s surface but little is known about their waters.
While the echosounders, often used for exploration of our oceans, have developed at
a tremendous rate since the WWII, the methods used to analyse and interpret the data
still remain the same. These methods are inefficient, time consuming, and often
costly in dealing with the large data that modern echosounders produce. This PhD
project will examine the complexity of the de facto seabed mapping technique by
exploring and analysing acoustic data with a combination of data mining and visual
analytic methods.
First we test the redundancy issues in multibeam echosounder (MBES) data
by using the component plane visualisation of a Self Organising Map (SOM). A total
of 16 visual groups were identified among the 132 statistical data descriptors. The
optimised MBES dataset had 35 attributes from 16 visual groups and represented a
73% reduction in data dimensionality. A combined Principal Component Analysis
(PCA) + k-means was used to cluster both the datasets. The cluster results were
visually compared as well as internally validated using four different internal
validation methods.
Next we tested two novel approaches in singlebeam echosounder (SBES)
data processing and clustering – using visual exploration for outlier detection and
direct clustering of time series echo returns. Visual exploration identified further
outliers the automatic procedure was not able to find. The SBES data were then
clustered directly. The internal validation indices suggested the optimal number of
clusters to be three. This is consistent with the assumption that the SBES time series
represented the subsurface classes of the seabed.
Next the SBES data were joined with the corresponding MBES data based on
identification of the closest locations between MBES and SBES. Two algorithms,
PCA + k-means and fuzzy c-means were tested and results visualised. From visual
comparison, the cluster boundary appeared to have better definitions when compared
to the clustered MBES data only. The results seem to indicate that adding SBES did
in fact improve the boundary definitions.
Next the cluster results from the analysis chapters were validated against
ground truth data using a confusion matrix and kappa coefficients. For MBES, the
classes derived from optimised data yielded better accuracy compared to that of the
original data. For SBES, direct clustering was able to provide a relatively reliable
overview of the underlying classes in survey area. The combined MBES + SBES
data provided by far the best accuracy for mapping with almost a 10% increase in
overall accuracy compared to that of the original MBES data.
The results proved to be promising in optimising the acoustic data and
improving the quality of seabed mapping. Furthermore, these approaches have the
potential of significant time and cost saving in the seabed mapping process. Finally
some future directions are recommended for the findings of this research project with
the consideration that this could contribute to further development of seabed
mapping problems at mapping agencies worldwide
A New Measure of Cluster Validity Using Line Symmetry
[[abstract]]Many real-world and man-made objects are symmetry, therefore, it is reasonable to assume that some kind of symmetry may exist in data clusters. In this paper a new cluster validity measure which adopts a non-metric distance measure based on the idea of "line symmetry" is presented. The proposed validity measure can be applied in finding the number of clusters of different geometrical structures. Several data sets are used to illustrate the performance of the proposed measure.[[notice]]補正完畢[[journaltype]]國外[[incitationindex]]SCI[[incitationindex]]EI[[ispeerreviewed]]Y[[booktype]]紙本[[booktype]]電子版[[countrycodes]]TW
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