G.B. Coleman and H.C. Andrews, "Image Segmentation by Clustering", Proc IEEE 67, pp. 773-785, (1979).  Home/Search   Document Not in Database   Summary   Related Articles   Check

....namely, K means, maximum likelihood, backpropagation neural network and histogram thresholding. Index Terms Image segmentation, cluster analysis, artificial neural networks, SAR imagery, target detection 1 Introduction Clustering based methods of image segmentation have demonstrated success [1] [4] Among clustering techniques that have been successfully utilized in image segmentation, K means and maximum likelihood (ML) 5] have received much attention [1] 4] Their popularity generally stems from their relative simplicity and low computational cost, especially when compared to ....

.... networks, SAR imagery, target detection 1 Introduction Clustering based methods of image segmentation have demonstrated success [1] 4] Among clustering techniques that have been successfully utilized in image segmentation, K means and maximum likelihood (ML) 5] have received much attention [1] [4] Their popularity generally stems from their relative simplicity and low computational cost, especially when compared to simulated annealingbased procedures [6] 7] 8] K means and ML, on the other hand, frequently suffer from the cluster underutilization problem [9] 12] This problem ....

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G. B. Coleman and H. C. Andrews, "Image segmentation by clustering," Proceedings of the IEEE, vol. 67, no. 5, pp. 773-785, 1979.

....in partitioning it into connected regions according to some homogeneity criterion. There is a difficult problem and the color quantization is commonly used as a preprocessing step for color image segmentation [5] Color image quantization can be viewed as a 3 dimensional data clustering problem [8] that consists in reducing the size of a finite set of data with a minimal loss of information by grouping data themselves. The set of data is partitioned into a set of clusters and a representative element is chosen in each cluster. The measurement of the error introduced by the clustering ....

Guy. B. Coleman and Harry C. Andrews. Image segmentation by clustering. Proceedings of the IEEE, 67(5):773--785, May 1979.

....considering the final weights vectors in the map as the new sample space. This new data set is used for clustering, allowing the determination of a set of cluster centers. The clustering process to group the SOM data is based on the method proposed by Coleman and Andrews for image segmentation [Coleman Andrews (1979)] With the availability of the features, grouping is performed in order to define the optimum number of clusters along with the center for each cluster. Then, in a subsequent phase, the assignment of each sample data to its closest cluster center is done. The clustering algorithm determines two ....

Coleman, G. B. and H. C. Andrews. Image segmentation by clustering. Proc. IEEE, 67(5): 773-785, 1979.

....Colour image segmentation; Intra cluster distance; Inter class distance. 1. Introduction Many approaches to image segmentation have been proposed over the years [1 12] Of these various methods, clustering is one of the simplest, and has been widely used in segmentation of grey level images [13 15]. Techniques such as k means [16] isodata [16] and fuzzy c means [17,18] have been around for quite a while, however, their application to colour images has been limited. Although colour images have increased dimensionality by requiring three bands such as red, green and blue, clustering ....

G.B. Coleman and H.C. Andrews, Image segmentation by clustering, Proc. IEEE, vol. 67, pp. 773-785, 1979.

....include the x and y coordinates of a pixel) The problem becomes more complex, due to the fact that clusters of the feature vectors are not necessarily connected in the xy plane. Various methods for finding an initial clustering and refining it to observe the spatial constraints have been studied [7, 24, 38, 36]. Jain and Dubes [23] discuss three types of images for which segmentation algorithms are employed, each of which determines a distinct feature vector. In the case of textured images, the vector of a pixel must reflect textural qualities such as coarseness or regularity. Various methods are used ....

G. Coleman and H. Andrews, Image segmentation by clustering, Proceedings of the IEEE., 1979, pp. 773--785.

.... are the adjustment of criteria for splitting and merging classes, for determining the number of classes that can be separated [3] and the sensitivity of clustering to initial parameter selection [5] There is even a discussion of peculiar characteristics of different clustering procedures [12]. The operator runs the algorithm with a set of parameters and hopes to obtain a clustering which results in the correct classification. Due to the complexity of the mathematical packages and their tuning parameters, it is difficult to correct classification errors. The influence of the parameters ....

Coleman G.B. and Andrews, H.C., Image Segmentation by Clustering, Proc. of the IEEE, Vol. 67, No. 5, May 1979, pp. 773-85

....of segmenting images on a textural basis. One of the primary problems is that the number of segments within the image is unknown (in the majority of cases) a priori. To this end, much effort has been directed at the application of unsupervised clustering methods to features extracted from images [11, 16, 20, 2, 6]. It is unreasonable to expect, however, for feature space data sets, constructed from the image, to have a simple hyper ellipsoidal cluster structure, and hence the use of algorithms such as K means may be inappropriate. We take a simple example in this paper and investigate image segmentation ....

Coleman and Andrews. Image Segmentation by Clustering. IEEE Proc., 67(5):773--785, 1979.

....e.g. 22 27, 11] In general, most colour texture representation schemes either use a combination of gray level texture features together with pure colour features, or they derive texture features computed separately in each of the three colour spectral channels. For example, Coleman and Andrews [28] used K means clustering in each colour band and maximised a cluster fidelity parameter for a more psychovisually acceptable segmented image. Tan and Kittler [29] used eight DCT texture features computed from the intensity image and six colour features derived from the colour histogram of a ....

G. B. Coleman and H. C. Andrews. Image segmentation by clustering. Proceedings of the IEEE, 67(5):773--785, 1979.

....namely, K means, maximum likelihood, backpropagation neural network and histogram thresholding. Index Terms Image segmentation, cluster analysis, artificial neural networks, SAR imagery, target detection 1 Introduction Clustering based methods of image segmentation have demonstrated success [1] [4] Among clustering techniques that have been successfully utilized in image segmentation, K means and maximum likelihood (ML) 5] have received much attention [1] 4] Their popularity generally stems from their relative simplicity and low computational cost, especially when compared to ....

.... networks, SAR imagery, target detection 1 Introduction Clustering based methods of image segmentation have demonstrated success [1] 4] Among clustering techniques that have been successfully utilized in image segmentation, K means and maximum likelihood (ML) 5] have received much attention [1] [4] Their popularity generally stems from their relative simplicity and low computational cost, especially when compared to simulated annealingbased procedures [6] 7] 8] K means and ML, on the other hand, frequently suffer from the cluster underutilization problem [9] 12] This problem ....

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G. B. Coleman and H. C. Andrews, "Image segmentation by clustering," Proceedings of the IEEE, vol. 67, no. 5, pp. 773-785, 1979.

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G.B. Coleman and H.C. Andrews, "Image Segmentation by Clustering", Proc IEEE 67, pp. 773-785, (1979).

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Guy B Coleman and Harry C Andrews. Image segmentation by clustering. Proceedings of the IEEE, 67(5):773-785, 1979.

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G. B. Coleman and H. C. Andrews. Image segmentation by Clustering, Proc. IEEE, 67:773-785, May 1979.

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G. B. Coleman and H. C. Andrews, "Image segmentation by clustering," Proceedings of the IEEE, vol. 67, no. 5, pp. 773-785, 1979.

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G.B. Coleman, H.C. Andrews, "Image segmentation by clustering," Proceedings of the IEEE, Vol. 67, No. 5, 1979. 12

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G. B. Coleman and H. C. Andrews, "Image segmentation by clustering," Proceedings of the IEEE 67(5), pp. 773-- 785, 1979.

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G. B. Coleman and H. C. Andrews, "Image segmentation by clustering," Proceedings of the IEEE, vol. 67, no. 5, pp. 773-785, 1979.

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G. B. Coleman and H. C. Andrews. Image segmentation by clustering. Proceedings of the IEEE, 69:773--785, 1979.

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Coleman and Andrews. Image Segmentation by Clustering. IEEE Proc., 67(5):773--785, 1979.

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