Results 171 to 180 of about 7,930 (201)

An Isodata algorithm for straight line fitting

Pattern Recognition Letters, 1988
Abstract This note describes a method of fitting κ straight lines to a set of data points using an algorithm analogous to the Isodata , or κ-means, clustering technique for partitioning a set of data points into κ compact clusters.
Tsai-Yun Phillips, Azriel Rosenfeld
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A dissimilarity criterion for sequential fuzzy isodata

Fuzzy Sets and Systems, 1987
A criterion for dissimilarity is provided so that new prototypes may be discovered when sample feature vectors are received sequentially in time, as input to the fuzzy ISODATA process.
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Pyramid linking is a special case of ISODATA

IEEE Transactions on Systems, Man, and Cybernetics, 1983
It is shown that the `pyramid linking' method of image segmentation can be regarded as a special case of the ISODATA clustering algorithm and hence is guaranteed to converge.
Simon Kasif, Azriel Rosenfeld
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Palmprint recognition based on isodata clustering algorithm

2007 International Conference on Wavelet Analysis and Pattern Recognition, 2007
This paper analyzes the palmprint textures with a multi-resolution method. Texture feature vectors of palmprint are extracted by wavelet transformation. With the texture feature, isodata clustering method is used to achieve classification of the Feature vectors.
null Fu Liu, null Cai-Xia Lin, null Ping-Yuan Cui, null Tian Dong   +3 more
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Hardware implementation of ISODATA and Otsu thresholding algorithms

2016 XXI Symposium on Signal Processing, Images and Artificial Vision (STSIVA), 2016
Image and video processing algorithms implemented in software, require most computation time when the image size is increased. Also, some algorithms must be processed at high-speed, for example the image thresholding algorithms for high throughput real-time applications.
Albert F. Torres-Monsalve, Jaime Velasco-Medina   +1 more
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A Convergence Theorem for the Fuzzy ISODATA Clustering Algorithms

IEEE Transactions on Pattern Analysis and Machine Intelligence, 1980
In this paper the convergence of a class of clustering procedures, popularly known as the fuzzy ISODATA algorithms, is established. The theory of Zangwill is used to prove that arbitrary sequences generated by these (Picard iteration) procedures always terminates at a local minimum, or at worst, always contains a subsequence which converges to a local ...
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ISODATA classification with parameters estimated by evolutionary approach

2013 8th International Conference on Intelligent Systems: Theories and Applications (SITA), 2013
The lsodata algorithm is an unsupervised data classification algorithm. Its result depends strongly on two parameters: distance threshold for the union of clusters and threshold of typical deviation for the division of a cluster. A bad choice of these two parameters leads the algorithm to spiral out of control leaving the end only one class.
M. Merzougui, M. Nasri, B. Bouali
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An SR-ISODATA algorithm for IDS alerts aggregation

2014 IEEE International Conference on Information and Automation (ICIA), 2014
Intrusion detection Systems(IDS) can produce large amount of alert data which usually possesses the characteristics of high redundancy and high repetition. Such kind of data makes the event processing for network security significantly difficult.
Chun Long, Hanji Shen, Jun Li, Jingguo Ge   +3 more
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An improved ISODATA algorithm for hyperspectral image classification

2014 7th International Congress on Image and Signal Processing, 2014
Hyperspectral image classification is an important part of the hyperspectral remote sensing information processing. The Iterative Selforganizing Data Analysis Techniques Algorithm (ISODATA) clustering algorithm which is an unsupervised classification algorithm is considered as an effective measure in the area of processing hyperspectral images. In this
Qian Wang, Qingli Li, Hongying Liu, Yiting Wang, Jianzhong Zhu   +4 more
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Convergence and Consistency of Fuzzy c-means/ISODATA Algorithms

IEEE Transactions on Pattern Analysis and Machine Intelligence, 1987
The fuzzy c-means/ISODATA algorithm is usually described in terms of clustering a finite data set. An equivalent point of view is that the algorithm clusters the support points of a finite-support probability distribution. Motivated by recent work on the hard version of the algorithm, this paper extends the definition to arbitrary distributions and ...
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