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基于改进粒子群算法的多阈值图像分割 总被引:1,自引:0,他引:1
提出了一种改进的粒子群算法,在初始化种群时采用相对基学习原理,以获得较优的初始候选解;在后期迭代过程中引入扩张模型,使粒子不易陷入局部极小值点,并将其用于多阈值图像分割。由最大熵阈值法得到所要优化的目标函数,用改进的粒子群算法对其进行优化,使其能够准确并迅速的得到分割的最佳阈值组合,并用该阈值组合对图像进行分割。将此分割结果与遗传算法的多阈值分割结果相比较可以看出,该算法可更为准确快速的实现图像分割。 相似文献
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This paper proposes a new approach for the segmentation of both near-end and far-end intima-media regions of the common carotid artery in ultrasound images. The method requires minimal user interaction and is able to segment the near-end wall in arteries with large, hypoechogenic and irregular plaques, issues usually not considered previously due to the increased segmentation difficulty. 相似文献
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An adaptive local binarization method for document images based on a novel thresholding method and dynamic windows 总被引:1,自引:0,他引:1
Bilal Bataineh Siti Norul Huda Sheikh Abdullah Khairuddin Omar 《Pattern recognition letters》2011,32(14):1805-1813
Binary image representation is essential format for document analysis. In general, different available binarization techniques are implemented for different types of binarization problems. The majority of binarization techniques are complex and are compounded from filters and existing operations. However, the few simple thresholding methods available cannot be applied to many binarization problems. In this paper, we propose a local binarization method based on a simple, novel thresholding method with dynamic and flexible windows. The proposed method is tested on selected samples called the DIBCO 2009 benchmark dataset using specialized evaluation techniques for binarization processes. To evaluate the performance of our proposed method, we compared it with the Niblack, Sauvola and NICK methods. The results of the experiments show that the proposed method adapts well to all types of binarization challenges, can deal with higher numbers of binarization problems and boosts the overall performance of the binarization. 相似文献
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Hon-Son Don 《International Journal on Document Analysis and Recognition》2001,4(2):131-138
A new thresholding method, called the noise attribute thresholding method (NAT), for document image binarization is presented
in this paper. This method utilizes the noise attribute features extracted from the images to make the selection of threshold
values for image thresholding. These features are based on the properties of noise in the images and are independent of the
strength of the signals (objects and background) in the image. A simple noise model is given to explain these noise properties.
The NAT method has been applied to the problem of removing text and figures printed on the back of the paper. Conventional
global thresholding methods cannot solve this kind of problem satisfactorily. Experimental results show that the NAT method
is very effective.
Received July 05, 1999 / Revised July 07, 2000 相似文献
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提出了一种将快速二维阈值化与模糊聚类相混合的图象分割方法,以进一步减少快速二维阈值分割中的噪声与错误分割。实验结果表明,利用这种方法分割信噪比较低的图象,能够在很短的时间内得到较为令人满意的分割结果。此外,本文还讨论了这一方法中隶属度函数的选取对分割结果的影响 相似文献
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Marte A. Ramírez-Ortegón Lilia L. Ramírez-Ramírez Volker Märgner Ines Ben Messaoud Erik Cuevas Raúl Rojas 《Pattern recognition》2014
In this paper, we will present a mathematical analysis of the transition proportion for the normal threshold (NorT) based on the transition method. The transition proportion is a parameter of NorT which plays an important role in the theoretical development of NorT. We will study the mathematical forms of the quadratic equation from which NorT is computed. Through this analysis, we will describe how the transition proportion affects NorT. Then, we will prove that NorT is robust to inaccurate estimations of the transition proportion. Furthermore, our analysis extends to thresholding methods that rely on Bayes rule, and it also gives the mathematical bases for potential applications of the transition proportion as a feature to estimate stroke width and detect regions of interest. In the majority of our experiments, we used a database composed of small images that were extracted from DIBCO 2009 and H-DIBCO 2010 benchmarks. However, we also report evaluations using the original (H-)DIBCO?s benchmarks. 相似文献
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Pei-Yan Pai 《Expert systems with applications》2012,39(1):154-161
In this paper, a nucleus and cytoplast contour detector (NCC detector) is presented to automatically detect the cytoplast and nucleus contours of a cell in a cervical smear image. The NCC detector uses the adaptable threshold decision (ATD) method to separate the cell from the cervical smear image, and then uses the maximal gray-level-gradient-difference (MGLGD) method, proposed in this paper, to extract the nucleus from the cell. The experimental results show that the NCC detector is superior to two existing methods, the gradient vector flow-active contour model (GVF-ACM) and the edge enhancement nucleus and cytoplast contour (ENNCC) detector, in segmenting the cytoplast and nucleus of a cell. 相似文献