26,120 research outputs found
Comparison of Various Improved-Partition Fuzzy c-Means Clustering Algorithms in Fast Color Reduction
This paper provides a comparative study of sev-
eral enhanced versions of the fuzzy
c
-means clustering al-
gorithm in an application of histogram-based image color
reduction. A common preprocessing is performed before clus-
tering, consisting of a preliminary color quantization, histogram
extraction and selection of frequently occurring colors of the
image. These selected colors will be clustered by tested
c
-means
algorithms. Clustering is followed by another common step,
which creates the output image. Besides conventional hard
(HCM) and fuzzy
c
-means (FCM) clustering, the so-called
generalized improved partition FCM algorithm, and several
versions of the suppressed FCM (s-FCM) in its conventional
and generalized form, are included in this study. Accuracy is
measured as the average color difference between pixels of the
input and output image, while efficiency is mostly characterized
by the total runtime of the performed color reduction. Nu-
merical evaluation found all enhanced FCM algorithms more
accurate, and four out of seven enhanced algorithms faster than
FCM. All tested algorithms can create reduced color images of
acceptable quality
An SMP Soft Classification Algorithm for Remote Sensing
This work introduces a symmetric multiprocessing (SMP) version of the continuous iterative
guided spectral class rejection (CIGSCR) algorithm, a semiautomated classiïŹcation algorithm for remote
sensing (multispectral) images. The algorithm uses soft data clusters to produce a soft classiïŹcation
containing inherently more information than a comparable hard classiïŹcation at an increased computational
cost. Previous work suggests that similar algorithms achieve good parallel scalability, motivating the parallel
algorithm development work here. Experimental results of applying parallel CIGSCR to an image with
approximately 10^8 pixels and six bands demonstrate superlinear speedup. A soft two class classiïŹcation is
generated in just over four minutes using 32 processors
Fast Color Quantization Using Weighted Sort-Means Clustering
Color quantization is an important operation with numerous applications in
graphics and image processing. Most quantization methods are essentially based
on data clustering algorithms. However, despite its popularity as a general
purpose clustering algorithm, k-means has not received much respect in the
color quantization literature because of its high computational requirements
and sensitivity to initialization. In this paper, a fast color quantization
method based on k-means is presented. The method involves several modifications
to the conventional (batch) k-means algorithm including data reduction, sample
weighting, and the use of triangle inequality to speed up the nearest neighbor
search. Experiments on a diverse set of images demonstrate that, with the
proposed modifications, k-means becomes very competitive with state-of-the-art
color quantization methods in terms of both effectiveness and efficiency.Comment: 30 pages, 2 figures, 4 table
Thermal error modelling of machine tools based on ANFIS with fuzzy c-means clustering using a thermal imaging camera
Thermal errors are often quoted as being the largest contributor to CNC machine tool errors, but they can be effectively reduced using error compensation. The performance of a thermal error compensation system depends on the accuracy and robustness of the thermal error model and the quality of the inputs to the model. The location of temperature measurement must provide a representative measurement of the change in temperature that will affect the machine structure. The number of sensors and their locations are not always intuitive and the time required to identify the optimal locations is often prohibitive, resulting in compromise and poor results.
In this paper, a new intelligent compensation system for reducing thermal errors of machine tools using data obtained from a thermal imaging camera is introduced. Different groups of key temperature points were identified from thermal images using a novel schema based on a Grey model GM (0, N) and Fuzzy c-means (FCM) clustering method. An Adaptive Neuro-Fuzzy Inference System with Fuzzy c-means clustering (FCM-ANFIS) was employed to design the thermal prediction model. In order to optimise the approach, a parametric study was carried out by changing the number of inputs and number of membership functions to the FCM-ANFIS model, and comparing the relative robustness of the designs. According to the results, the FCM-ANFIS model with four inputs and six membership functions achieves the best performance in terms of the accuracy of its predictive ability. The residual value of the model is smaller than ± 2 Όm, which represents a 95% reduction in the thermally-induced error on the machine. Finally, the proposed method is shown to compare favourably against an Artificial Neural Network (ANN) model
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