Image segmentation is the process of partitioning the image into regions of interest in order to provide a meaningful representation of information. Nowadays, segmentation has become a necessity in many practical medi...
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ISBN:
(纸本)9783319897431;9783319897424
Image segmentation is the process of partitioning the image into regions of interest in order to provide a meaningful representation of information. Nowadays, segmentation has become a necessity in many practical medical imaging methods as locating tumors and diseases. Hidden Markov Random Field model is one of several techniques used in image segmentation. It provides an elegant way to model the segmentation process. This modeling leads to the minimization of an objective function. conjugategradientalgorithm (CG) is one of the best known optimization techniques. This paper proposes the use of the nonlinear conjugategradientalgorithm (CG) for image segmentation, in combination with the Hidden Markov Random Field modelization. Since derivatives are not available for this expression, finite differences are used in the CG algorithm to approximate the first derivative. The approach is evaluated using a number of publicly available images, where ground truth is known. The Dice Coefficient is used as an objective criterion to measure the quality of segmentation. The results show that the proposed CG approach compares favorably with other variants of Hidden Markov Random Field segmentation algorithms.
This paper presents the hybrid algorithm of global optimization of dynamic learning rate for multilayer feedforward neural networks (MLFNN).The effect of inexact line search on conjugacy was studied, based on which a ...
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This paper presents the hybrid algorithm of global optimization of dynamic learning rate for multilayer feedforward neural networks (MLFNN).The effect of inexact line search on conjugacy was studied, based on which a generalized conjugategradient method was proposed, showing global convergence for error backpagation of MLFNN. It overcomes the drawback of conventional BP and Polak-Ribieve conjugategradientalgorithms that maybe plunge into local minima. The hybrid algorithm's recognition rate is higher than that of Polak-Ribieve algorithm and convergence BP for test data, its training time is less than that of Fletcher-Reeves algorithm and far less than that of convergence BP, and it has a less complicated and stronger robustness to real speech data.
This paper presents the hybrid algorithm of global optimization of dynamic learning rate for multilayer feedforward neural networks(MLFNN). The effect of inexact line search on conjugacy was studied and a generalized ...
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This paper presents the hybrid algorithm of global optimization of dynamic learning rate for multilayer feedforward neural networks(MLFNN). The effect of inexact line search on conjugacy was studied and a generalized conjugategradient method based on this effect was proposed and shown to have global convergence for error backpagation of *** descent property and global convergence was given for the improved hybrid algrithm of conjugategradientalgorithm, the results of the proposed algorithm show a considerable improvement over the FletcherRreeves algorithm and conventional BP algorithm, it overcomes the drawback of conventional BP and Polak-Ribieve conjugategradientalgorithm that maybe plung into local minima.
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