数字图像分割.doc
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数字图像分割,摘要图像分割是一种关键的图像分析技术，目的是通过对图像的分析和研究，将感兴趣的区域或目标提取出来。图像分割是承接图像处理与图像分析之间的关键步骤，也是图像进一步理解的基础。图像分割有着篅@さ难芯坷罚恢笔茄芯康娜鹊愫徒沟阄侍猓甘昀匆蔡岢隽耸郧Ъ频乃惴āＵ庑┓椒ㄋ淙辉谝欢ǔ潭群头段诮饩隽四承┨囟ǖ奈侍猓遣⒉...
内容介绍
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摘 要
图像分割是一种关键的图像分析技术，目的是通过对图像的分析和研究，将感兴趣的区域或目标提取出来。图像分割是承接图像处理与图像分析之间的关键步骤，也是图像进一步理解的基础。图像分割有着篅@さ难芯坷罚恢笔茄芯康娜鹊愫徒沟阄侍猓甘昀匆蔡岢隽耸郧Ъ频乃惴āＵ庑┓椒ㄋ淙辉谝欢ǔ潭群头段诮饩隽四承┨囟ǖ奈侍猓遣⒉荒芙饩鏊型枷穹指畹奈侍狻２⑶遥裎挂裁挥幸桓鐾ㄓ玫睦砺劾雌兰鄯指畹慕峁虼苏夥矫娴难芯棵媪傩矶嗵粽健�
在分割图像时，由于受到噪声、光照等污染使得图像模糊不清，图像中的细节和边缘等信息无法完全分割出来，本文研究的基于模糊技术的图像分割能对模糊降质图像进行有效分割。
本文对数字图像分割方法做了系统深入的研究，主要研究工作如下：
1、分析了图像分割的研究背景，概括了国内外的研究现状和发展趋势。
2、深入研究了图像分割前期的预处理问题。分析比较了抑制高斯噪声的均值滤波器和抑制椒盐噪声的中值滤波器。在此基础上针对图像通常含有的噪声类型，提出了一种改进的PCNN脉冲耦合神经网络图像滤波算法。该方法是通过对每个神经元进行受噪类型的判断，选择使用不同的去噪滤波器。
3、基于模糊理论的阈值图像分割方法研究。详细分析了图像分割中的阈值分割方法，针对待分割图像均或多或少存在模糊不清的情况，结合模糊理论，将模糊技术与大津法（Otsu）阈值分割相融合改进了阈值图像分割方法。实验表明该方法可以改进因噪声、光照或其他干扰因素造成的模糊、多目标、分割不完全的情况，分割效果明显改善。
4、改进传统大津图像分割方法。为更有效地解决自动选取的阈值偏向于方差较大一类的问题，准确找到直方图的谷点位置，更好地分割小对象，改进了传统大津法。
5、结合遗传算法的阈值图像分割方法研究。为提高分割效率，研究了图像分割的快速性。利用寻优性能较好的智能算法中的遗传算法来对多阈值寻优，实验结果表明该方法可以较准确寻找到一组最优解，且耗费时间比模拟退火算法(SA)及穷举法要少得多。同时与Otsu、最大熵方法比较，结合遗传算法的分割方法所耗费的时间要略多一些，但是却能获得更高质量的图像分割结果。折中的结果是结合遗传算法的分割方法能取得更好的效果。
关键词 图像分割；滤波去噪；最大类间方差阈值；模糊技术；遗传算法
Abstract
Image segmentation is a key image analysis technology, which the purpose is to pick out the regions or objectives of interest through analysis and study of the image. Image segmentation is an important step between image processing and image analysis, it is also the foundation of the further image understanding. Image segmentation has a long research history; it has been a hot research and focus, and thousands of algorithms were proposed. Although these methods have some extent and within a certain range solved some specific problems, cannot solve all the problems of image segmentation. Furthermore, there is not a general theory to eva luate the results of segmentation, so the research in this area faces many challenges.
In the image segmentation, the details, edges and other information in images cannot be completely separated because of image noises, light, etc. So the image segmentation based on fuzzy technology can effectively segment the fuzzy degraded images.
The paper studies the methods of digital image segmentation. The main works include:
1. Analyzed the background of image segmentation, summarized the status of research and the development trend in domestic and foreign.
2. Studied the pretreatment of the pre-segmentation, including compared the mean filter which can suppress Gaussian noise and the median filter which can suppress Salt & Pepper noise. On this basis, considering the types of noise which the images usually contain, proposed an improved PCNN (Pulse Coupled Neural Network) image filter algorithm. The method used different noise filters according to the different types of noise which each neuron was polluted.
3. Research on threshold segmentation based on fuzzy theory. Analyzed the thresholding image segmentation in details, and after studying the fuzzy technology, proposed a new method which combined with fuzzy technology and Otsu thresholding segmentation. It can deal with the images which are fuzzy. The experiment results showed that it can improve the fuzzy, multi-objective, incomplete segmentation situation caused by noises, light and other interference factors.
4. Improved the traditional Otsu, so it can overcome the thresholds trends to larger variance automatically, find the valley of histogram accurately. It can segment the small objective better.
5. Research on threshold segmentation based on genetic algorithm. To improve the segmentation efficiency, studied the image segmentation quickness. It used genetic algorithm which is one of the best optimization algorithms to optimize the multiple thresholds. The experiment results signified the method can find a set of optimal solutions accurately and the time-consuming is much less than the Simulated Annealing (SA) and the exhaustive method. At the same time, this method spent slightly more time than the Otsu and the maximum entropy method, but it can gain better segmentation effect. Compromise is the new segmentation with genetic algorithm can achieve better results.
Keywords image seg..
图像分割是一种关键的图像分析技术，目的是通过对图像的分析和研究，将感兴趣的区域或目标提取出来。图像分割是承接图像处理与图像分析之间的关键步骤，也是图像进一步理解的基础。图像分割有着篅@さ难芯坷罚恢笔茄芯康娜鹊愫徒沟阄侍猓甘昀匆蔡岢隽耸郧Ъ频乃惴āＵ庑┓椒ㄋ淙辉谝欢ǔ潭群头段诮饩隽四承┨囟ǖ奈侍猓遣⒉荒芙饩鏊型枷穹指畹奈侍狻２⑶遥裎挂裁挥幸桓鐾ㄓ玫睦砺劾雌兰鄯指畹慕峁虼苏夥矫娴难芯棵媪傩矶嗵粽健�
在分割图像时，由于受到噪声、光照等污染使得图像模糊不清，图像中的细节和边缘等信息无法完全分割出来，本文研究的基于模糊技术的图像分割能对模糊降质图像进行有效分割。
本文对数字图像分割方法做了系统深入的研究，主要研究工作如下：
1、分析了图像分割的研究背景，概括了国内外的研究现状和发展趋势。
2、深入研究了图像分割前期的预处理问题。分析比较了抑制高斯噪声的均值滤波器和抑制椒盐噪声的中值滤波器。在此基础上针对图像通常含有的噪声类型，提出了一种改进的PCNN脉冲耦合神经网络图像滤波算法。该方法是通过对每个神经元进行受噪类型的判断，选择使用不同的去噪滤波器。
3、基于模糊理论的阈值图像分割方法研究。详细分析了图像分割中的阈值分割方法，针对待分割图像均或多或少存在模糊不清的情况，结合模糊理论，将模糊技术与大津法（Otsu）阈值分割相融合改进了阈值图像分割方法。实验表明该方法可以改进因噪声、光照或其他干扰因素造成的模糊、多目标、分割不完全的情况，分割效果明显改善。
4、改进传统大津图像分割方法。为更有效地解决自动选取的阈值偏向于方差较大一类的问题，准确找到直方图的谷点位置，更好地分割小对象，改进了传统大津法。
5、结合遗传算法的阈值图像分割方法研究。为提高分割效率，研究了图像分割的快速性。利用寻优性能较好的智能算法中的遗传算法来对多阈值寻优，实验结果表明该方法可以较准确寻找到一组最优解，且耗费时间比模拟退火算法(SA)及穷举法要少得多。同时与Otsu、最大熵方法比较，结合遗传算法的分割方法所耗费的时间要略多一些，但是却能获得更高质量的图像分割结果。折中的结果是结合遗传算法的分割方法能取得更好的效果。
关键词 图像分割；滤波去噪；最大类间方差阈值；模糊技术；遗传算法
Abstract
Image segmentation is a key image analysis technology, which the purpose is to pick out the regions or objectives of interest through analysis and study of the image. Image segmentation is an important step between image processing and image analysis, it is also the foundation of the further image understanding. Image segmentation has a long research history; it has been a hot research and focus, and thousands of algorithms were proposed. Although these methods have some extent and within a certain range solved some specific problems, cannot solve all the problems of image segmentation. Furthermore, there is not a general theory to eva luate the results of segmentation, so the research in this area faces many challenges.
In the image segmentation, the details, edges and other information in images cannot be completely separated because of image noises, light, etc. So the image segmentation based on fuzzy technology can effectively segment the fuzzy degraded images.
The paper studies the methods of digital image segmentation. The main works include:
1. Analyzed the background of image segmentation, summarized the status of research and the development trend in domestic and foreign.
2. Studied the pretreatment of the pre-segmentation, including compared the mean filter which can suppress Gaussian noise and the median filter which can suppress Salt & Pepper noise. On this basis, considering the types of noise which the images usually contain, proposed an improved PCNN (Pulse Coupled Neural Network) image filter algorithm. The method used different noise filters according to the different types of noise which each neuron was polluted.
3. Research on threshold segmentation based on fuzzy theory. Analyzed the thresholding image segmentation in details, and after studying the fuzzy technology, proposed a new method which combined with fuzzy technology and Otsu thresholding segmentation. It can deal with the images which are fuzzy. The experiment results showed that it can improve the fuzzy, multi-objective, incomplete segmentation situation caused by noises, light and other interference factors.
4. Improved the traditional Otsu, so it can overcome the thresholds trends to larger variance automatically, find the valley of histogram accurately. It can segment the small objective better.
5. Research on threshold segmentation based on genetic algorithm. To improve the segmentation efficiency, studied the image segmentation quickness. It used genetic algorithm which is one of the best optimization algorithms to optimize the multiple thresholds. The experiment results signified the method can find a set of optimal solutions accurately and the time-consuming is much less than the Simulated Annealing (SA) and the exhaustive method. At the same time, this method spent slightly more time than the Otsu and the maximum entropy method, but it can gain better segmentation effect. Compromise is the new segmentation with genetic algorithm can achieve better results.
Keywords image seg..
