A fuzzy logic clustering algorithm to classify a given image into targets and backgrounds is presented. The algorithm forms clusters and is trained without supervision. The clustering is done on the basis of the statistical properties of the set of inputs. The algorithm features an adaptive mechanism for selecting the number of clusters, and it features an adaptive threshold. The problem of threshold selection is considered and the convergence of the algorithm is shown. The algorithm also does not require the number of clusters been known a priori. An example is given to illustrate the application of the algorithm.
A self-organizing k-means algorithm to classify the inputs (data) into classes is presented. This algorithm provides solutions to the problems that the k-means classification algorithm faces. The k-means classification algorithm has the problem of selecting the threshold(s). It also requires that the number of classes be known a priori. This algorithm forms clusters, removes noise, and is trained without supervision. The clustering is done on the basis of the statistical properties of the set of input data. The algorithm consists of two phases. The first phase is similar to the Carpenter/Grossberg classifier, and the second phase is a modified version of the k-means algorithm. An example is given to illustrate the application of this algorithm and to compare this algorithm with the k-means algorithm.
Multiple-class identification algorithm using genetic neural networks is presented. The algorithm uses a feedforward neural network so it is fast. The algorithm uses the Kohonen network to provide an unsupervised learning. The Kohonen network is used with Z-axis normalization. The weight initialization is done by genetic optimization to escape from local minima. The performance of the algorithm is evaluated using a confusion matrix method. The algorithm does not require the number of classes to be known a priori. It also provides a threshold selection method. An example is given to illustrate the application of the algorithm and to evaluate its performance.
A new data clustering algorithm using a self organizing method is presented. This algorithm forms clusters and is trained without supervision. The clustering is done on the basis of the statistical properties of the set of data. This algorithm differs from the K-means algorithm and other clustering algorithms in that the number of desired clusters is not required to be known a priori. It also removes noise and is fast. The convergence of the algorithm is shown. An example is given to show the application of the algorithm to clustering data and to compare the results obtained using this algorithm with those obtained using the K-means algorithm.
A multi-target and multi-background classification algorithm using neural networks is presented. The algorithm uses a feedforward neural network algorithm, a double window filter, and thresholds to classify an image into targets and backgrounds. This algorithm's performance differs from that of the K-nearest neighbor (K-NN) classifier algorithm in that (1) it provides noiseless classification, (2) it is faster, and (3) it provides better accuracy. Examples are given to illustrate the results.
A modern identification algorithm to reduce the complexity of estimating parameters for discrete time-invariant linear systems and nonlinear systems is presented. The algorithm requires no a priori knowledge of the input or of the order of the system. An identification unbiased estimator method is presented which reduces the computational complexity of covariance matrix inversion. Probability one convergence of the estimated parameters to their true values is presented, and stability of the identification algorithm is discussed. An example is presented to illustrate the results.
KEYWORDS: Detection and tracking algorithms, Microsoft Foundation Class Library, Sensors, Data fusion, Image fusion, Computer engineering, Lanthanum, Statistical analysis, Classification systems, Data processing
A multisensor fusion algorithm to classify the inputs (data or images) into classes (targets, backgrounds) is presented. The algorithm forms clusters and is trained without supervision. The clustering is done on the basis of the statistical properties of the set of inputs. This algorithm implements a clustering algorithm that is very similar to the simple sequential leader clustering algorithm and the Carpenter/Grossberg net algorithm (CGNA). The algorithm differs from CGNA in that (1) the data inputs and data pointers may take on real values, (2) it features an adaptive mechanism for selecting the number of clusters, and (3) it features an adaptive threshold. The problem of threshold selection is considered and the convergence of the algorithm is shown. An example is given to show the application of the algorithm to multisensor fusion for classifying targets and backgrounds.
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