马义德
信息传输与处理、计算机应用、数字图像处理技术、生物医学工程等
个性化签名
- 姓名:马义德
- 目前身份:
- 担任导师情况:
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学术头衔:
博士生导师
- 职称:-
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学科领域:
计算机应用
- 研究兴趣:信息传输与处理、计算机应用、数字图像处理技术、生物医学工程等
马义德,博士,兰州大学信息科学与工程学院教授(博士生导师),现为兰州大学信息科学与工程学院副院长。电路与系统研究所所长。长期从事信息传输与处理、计算机应用、数字图像处理技术、生物医学工程等专业教学科研工作。特别是多年来,主持的脉冲耦合神经网络(PCNN)课题组是国内最早进行PCNN理论研究及其应用探讨的团队之一,在PCNN理论发展及应用研究方面作了特色工作, 掀起国内对 PCNN研究热潮。专著《脉冲耦合神经网络原理及应用》已列入科学出版社由著名资深院士吴文俊先生任编委和名誉主编的智能信息科学著作系列丛书,并于2006年4月出版。在高等教育出版社、科学出版社、电子工业出版社、清华大学出版社等出版《微型计算机原理及其应用》、《微型计算机显示器电路分析及故障维修实例555》、《彩色电视自装遥控器原理与技术》等著作、教材11部,其中由科学出版社、电子工业出版社和电子科技大学出版社出版专著4部;发表学术论文近100多篇,其中8篇发表在SCI、EI源刊《Chinese Science Bulletion》、《Chinese Journal of Electronics》,20篇被SCI、EI索引,15篇发表在IEEE等国际学术会议,50多篇发表在《科学通报》、《通信学报》、《电子学报》、《电子科学学刊》、《系统仿真学报》等核心学术期刊。在生物医学图像信息量化分析与处理、生物细胞图像预处理等方面先后做了很多工作,获得国家自然基金、生物医学图像处理新技术研究方向的985特色项目和甘肃省自然科学基金等多项支持;与企业合作搞的横向科研项目涵盖了DSP、USB、PCI软硬件研制, 指纹、虹膜识别技术研究, 生物医学分析系统研制等领域,申请了USB接口通信装置等专利2项。
主讲的面向21世纪信息学科主干课程《微型计算机原理及应用》于2005年建设成为甘肃省精品课程,收录到国家精品课程频道网站,编写的教材于高等教育出版社出版,结合该课程的教学与科研成果获得甘肃省教学成果奖和兰大学教学成果奖。先后获得多项教学和科研奖励:宝钢优秀教师奖、甘肃省高校青年教师成才奖、甘肃省自然科学奖、甘肃省教学成果奖。甘肃省教育厅科技进步奖等多项。作为全国大学生电子竞赛指导老师指导学生获得全国大学生电子竞赛一等奖、甘肃省特等奖等多项。
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633
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10
马义德, , 戴若兰, △, 李廉, 吴承虎
生物医学工程学杂志,2002,19(3):487~492,-0001,():
-1年11月30日
阐述了小波变换、遗传算法、模糊数学、神经网络、数学形态学等生物细胞图像分割算法以及边缘检测、区域分割等传统图像分割算法为主的生物细胞图像分割技术的发展现状,指明了生物细胞图像本身具有的复杂性、多样性、各自差异性等属性是实现生物细胞图像全自动分割的难点,只有彻底结合生物视觉特性数学模型算法的研究和应用,才能使生物细胞图像全自动分割成为可能。
生物细胞, 图像分割, 小波变换, 模糊数学, 神经网络
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【期刊论文】一种基于脉冲耦合神经网络和图像熵的自动图像分割方法
马义德, , 戴若兰, 李廉
通信学报,2002,23(1):46~51,-0001,():
-1年11月30日
90年代发展形成的脉冲耦合神经网络(PCNN)模型特别适合于图像分割,边缘提取等方面的应用研究,但众所周知,PCNN模型图像分割效果不但取决于PCNN模型中各个参数的合理选择,而且同时还取决于循环迭代次数的确定选择准则,通常循环迭代次数N的选择通过人工交互方式来确。定正因如此选择合适的准则来确定N是PCNN图像分割的关键,但目前还没有文献提出一个合适的准则来解决这个问题。本文结合图像统计特性和PCNN参数模型提出了熵值最大准则。该准则实现了PCNN神经网络的自动图像分割。对于PCNN的理论研究和实际应用具有非常重要的现实意义。
脉冲耦合神经网络, 图像分割, 熵, 统计特性
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马义德, 马义德①, ②李廉②, *, 戴若兰①, 吴承虎②
科学通报,2001,46(21):1781~1786,-0001,():
-1年11月30日
植物胚性细胞定量分析研究需要首先将其切片图像分割处理,然后进行大分子量化分析但植物细胞切片图像上表现出来的植物细胞特有的复杂属性,使得一般图像分割分析方法很难奏效20世纪90年代中期发展起来的脉冲耦合神经网络PCNN直接来自于动物视觉特性研究,应当适合这类植物细胞图像的分割处理但因目前理论很难解释PCNN数学模型参数与图像分割效果之间的关系,一般较好图像分割效果的获得需多次实验选择这些模型参数同时在模型参数选定的情况下,其循环迭代次数直接关系到分割结果的好坏,而分割好坏的判定需人眼观察分析,这样便引入了人为干预为此提出一种建立在分割图像熵值最大原则上的PCNN植物细胞图像自动分割新算法。
植物胚性细胞, 脉冲耦合神经网, 络熵, 图像分割
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【期刊论文】Automated Image Segmentation Using Improved PCNN Model Based on Cross-entropy
马义德, MA Yi-de, LIU Qing, , QIAN Zhi-bai
October 20-22, 2004 Hong Kong,-0001,():
-1年11月30日
Pulse Coupled Neural Networks(PCNN) is a new Neural Networks which was developed and formed in the 1990's. The key point of PCNN is modulated coupled mechanism, while coupled results produce internal activity. The output of PCNN is binary image sequence, which can be considered the results of threshold segmentation. In this paper, the matrix made by internal activity is regarded as a breadth of image, then which can be conjoined with the technique of traditional threshold segmentation. The application of minimum cross-entropy criterion in the technique of image segmentation makes the discrepancy of information content between image segmented and image after segmentation to be least. A kind of novel algorithm of image segmentation setting on cycle iterations automatically is put forward, after traditional PCNN threshold segmentation mechanism improved with the combination of minimum cross-entropy criterion. Theory analysis and experimental results all show that the best segmentation output can be drew from the simple and sophisticated image using this new algorithm.
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【期刊论文】A NEW KIND OF IMPULSE NOISE FILTER BASED ON PCNN
马义德, Ma Yi-de, Shi Fei, Li Lian
,-0001,():
-1年11月30日
Median filter can inhibit the impulse noise in the image, butit always erodes or dilates the edges of images. H.S.Ranganath mentioned that impulse noise could beremoved through modifying the intensity of those contaminated pixels step by step using PCNN. Obviously this method consumes much more time in computation. Combining the PCNN model with the median filter, this paper presents an impulse noise filter based on a simplified PCNN model which has less parameters. Not only can it remove the impulse noise effectively, but also it keeps the details of images as can as possible. It can be verified through experiments and theory analysis that this kind of filter is superior to the normal median filter and the filter mentioned by H.S.Ranganath, no matter in the aspect of noise removal or in the aspect of keeping details.
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【期刊论文】GAUSSIAN NOISE FILTER BASED ON PCNN
马义德, Ma Yi-de, Shi Fei, Li Lian
,-0001,():
-1年11月30日
Pulse Coupled Neural Network (PCNN) has gained widely research as a new artificial neural network. It derives directly from the studies of the small mammal's visual cortex. PCNN is a model with multiple parameters, and finding the proper values of these parameters is an onerous task. So a simplified PCNN is put forward and its performance in removing Gaussian noise of image is discussed in this article. The algorithm of PCNN combined with median filter and the step-by-step modifying algorithm, which is also based on PCNN, are proposed, and the experiment results of the two algorithms are analyzed and compared with that of median filter and wiener filter.
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马义德, Ma Yi-de, Yang Miao
,-0001,():
-1年11月30日
A method for optimal complex rank-order morphological filters with hybrid genetic algorithm is presented in this paper. It combined simulated annealing genetic algorithm (SAGA) with adaptive genetic algorithm (AGA) to achieve optimal filtering parameters in a global searching. Experimental results show that this method is practical, easy to extend, and improves the performances of the complex rank-order morphological filters. By means of adaptive optimizing training the percentile and the structuring elements, morphological filters possess the shape and structural system characteristics of image targets. Complex rank-order morphological filters formed in this way become intelligent and can provide good filtering results and robust adaptability to image targets with clutter background.
order statistics,, complex rank-order morphological filters,, genetic algorithms,, optimization algorithms,, image processing
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【期刊论文】An Improved Algorithm Based on Extremum and Median Value*
马义德, MA Yide, YANG Miao and ZHANAG Xiangguang
,-0001,():
-1年11月30日
The extremum and median filter can not only preserve the details of image as much as possible but aslo remove the noise when the grayscale changed gently. But, it's not very effective when processing those images including extremum areas. A new algorithm is presented in this paper and it solves the problem is presented in this paper and it solves the problem existing in formet filter. The experimental results show that the method is quite effective on most images and has better performance.
Nonlinear filter,, Median fllter,, Extremum median filter,, Similaity function.,
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马义德, MA Yide, , DAI Rolan and LI Lian
Chinese Journal of Electronics Vol. 11, No.1, Jan. 2002,-0001,():
-1年11月30日
A new, simple method of counting and segmenting cell image is suggested in this paper. It is based on the feature of cell's logical and morphological iafornmation. By using of mathematics, n, morphological, logical operation and laplacian fiter, the method is realized with the MATLAH 5.10. The segment effect of this algorithm is tested with blood cell image in this article and the result is desirable, Particularly this method can count the number of blood cells. However, this counting is not very accurate, but it is enough for biological research of cytologlcal count. At the same time this method can segment special blood cell from its neighhorhood, which is very important for the development of image scgment technology, because traditional image segment method is inadequate to achieve such segment and count of calls.
Logical operation,, Cell image,, Mor phalogical operation,, Laplaclan filter.,
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【期刊论文】Image segmentation of em-bryonic plant cell using pulse-coupled neural networks
马义德, MA Yide, , DAI RolanT, LI Lian & WEI Lin
Chinese Science Bulletin Vol. 47 No.2 January 2002,-0001,():
-1年11月30日
Traditional image segmentation algorithms exhibit weak performance for plant cells which have complex structure. On the other hand, pulse-coupled neural network (PCNN) based on Eckhorn's model of the cat visual cortex should be suitable to the segmentation of plant cell image. But the present theories cannot explain the relationship be. tween the parameters of PCNN mathematical model and the effect of segmentation. Satisfactory results usually require time.consuming selection of experimental parameters. Mean-while, in a proper, selected parametric model, the number of iteration determines the segmented effect evaluated by visual judgment, which decreases the efficiency of image segmentation. To avoid these flaws, this note proposes a new PCNN algorithm for automatically segmenting plant embryonic cell image based on the maximum entropy principle. The algo-rithm produces a desirable result. In addition, a model with proper parameters can automatically determine the number of iteration, avoid visual judgment, enhance the speed of' segmentation and will be utilized subsequently by accurate quantitative analysis of micro.molecules of plant cell. So this algorithm is ainable for theoretical investigation and application of PCNN.
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