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1.
深入研究了一种二维细胞自动机(CA),找到了两个算法规则可以用来实现二值字符平滑和图像去噪处理,并且用这些规则设计了三种新的细胞神经网络(CNN).仿真结果证明这些CNN是简单而有效的,同时也证明了文中算法的合理性,为CNN的设计找到了一种新颖、有效的方法.  相似文献   

2.
细胞神经网络图像恢复新方法的研究   总被引:1,自引:0,他引:1  
结合二维细胞自动机(CA)和细胞神经网络通用二进制神经元(CNN-UBN)设计了两种新的用于噪声图像恢复的细胞神经网络(CNN)。这两种网络的联合处理图像结果明显优于长度为3和5的普通中值滤波恢复方法,且具有速度快、结构简单、易于硬件集成等优点,是一种新颖实用的图像恢复算法。  相似文献   

3.
针对常规水印算法对JPEG压缩与几何攻击鲁棒性较差的问题,提出了一种基于细胞自动机(CA)变换与奇异值分解(SVD)的零水印算法。首先对图像进行二维细胞自动机变换,分离出低频近似图像并保存作为密钥的变换参数;然后对低频图像分块并在每个子块上进行奇异值分解,通过细胞自动机变换规则在每个子块的奇异值矩阵上构造零水印;最后,图像认证时根据两个图像的水印相似度是否大于阈值来判断图像是否遭遇篡改。通过实验证明,该水印算法具有良好的不可见性和安全性,并且对于JPEG压缩与几何攻击表现出较强的鲁棒性。  相似文献   

4.
胡月  孙江林  周庆 《计算机工程》2010,36(23):110-112
传统的hash函数难以实现并行计算,用于图像认证时不能充分利用图像的特性。而二维细胞自动机的拓扑结构适用于图像,支持并行计算,且计算效率较高,由此提出一种基于二维细胞自动机的图像认证方法。理论分析与初步实验结果表明,细胞自动机随机性好、满足雪崩准则,效率高于传统的hash函数。  相似文献   

5.
为解决噪声显微细胞图像的多阈值分割问题,该文提出基于均值和梯度共生矩阵模型的最大熵多阈值算法。选用象素点的邻域灰度均值和梯度值构成二维灰度直方图。因为对象素点取均值可以平滑噪声,取梯度值可以锐化边缘,所以该算法能够改善图像的分割质量。考虑显微细胞图像多阈值分割的要求,该算法对二维灰度直方图采用改进的区域划分方式。通过优化传统的求熵算法,来减少运算时间,使之更加适合于擅长矩阵运算的MATLAB编程语言,从而提高运算速度。实验证明,该算法去除了噪声干扰,实现了显微细胞图像的多阈值分割,运算速度较快。  相似文献   

6.
显微细胞图像的识别方法研究   总被引:3,自引:0,他引:3  
张志民  赵晖 《计算机应用》2005,25(Z1):240-242
提出了一种自动识别显微细胞的方案.该方案首先采用二维阈值化和Canny算子分割方法对图像进行分割,并采用遗传算法将所得结果加以融合.分割之后,对每个细胞进行二值化处理,然后利用一种改进的区域增长法求出二值图像中黑色区域和白色区域的三个区域特征.最后用这些特征值训练BP神经网络,并使用训练好的神经网络来识别未知细胞.  相似文献   

7.
《微型机与应用》2015,(17):39-42
本文研究了一种显微细胞图像有形成分分割方法。首先,利用传统的边缘检测及阈值分割法对显微细胞图像有形成分进行分割比较,然后基于显微细胞图像特点提出了一种改进的二维最大熵阈值结合形态学分割方法。最后通过分割实验进行验证,结果表明利用本文方法能较好地实现显微细胞图像有形成分分割。所以本文提出的分割方法在医学上具有一定的实用价值。  相似文献   

8.
二维Otsu自适应阈值分割算法的改进   总被引:11,自引:0,他引:11  
在二维OTSU自适应阈值分割算法的基础上提出了一种改进的自适应阈值分割算法,这种改进算法由于充分考虑了图像二维直方图中象素灰度值及其领域平均灰度值比较接近的区域而获得了比传统算法具有更强抗噪声能力的分割算法,通过将该算法用于显微细胞图像的分割证明了它不仅分割效果得到改善,同时还大大降低了算法的复杂性。  相似文献   

9.
为了对细胞多光谱图像进行快速、准确的分割,首先探讨了光谱比值在细胞多光谱显微图像分割中的应用,然后提出了利用多光谱图像的光谱信息,并结合传统分割方法的一种新的细胞自动分割方法。该方法首先通过从扣除背底后的多光谱图像中选择两个波段图像进行光谱比值操作来生成一幅比率图像,然后对该图像进行自动多阈值分割、二值形态学操作,最终获得了细胞的胞浆和胞核覆盖层。该方法首次将光谱比值技术应用到细胞多光谱显微图像分割中,对骨髓细胞图像的自动分割实验表明,该方法具有分割准确、分割速度快、受外界干扰少的特点,该方法也可以推广到其他多光谱显微图像的分割中。  相似文献   

10.
针对细胞图像特性微弱,提出改进的Chan-Vese水平集分割算法,利用水平集演化曲线实现对微生物感染的细胞核边缘的准确分割;针对宫颈细胞图像特征,首先提取细胞图像样本的五维特征向量(纹理、几何形状特征以及灰度图像等),然后根据粗糙集理论建立决策表,用于细胞图像的微生物识别,并直接从训练样本图像中导出决策规则;应用所获取的规则对微生物测试样本图像进行分类;实验结果表明该方案应用在宫颈细胞智能识别上可以达到较好的效果,在工程上应用是可行的。  相似文献   

11.
In this paper, a cellular automaton (CA) is proposed as a tool for designing distributed scheduling algorithms for allocating parallel program tasks in multiprocessor systems. For this purpose, a program graph is considered as a CA containing elementary automata interacting locally according to some rules. In the first phase of the algorithm, effective rules for the CA are discovered by a genetic algorithm. In the second phase, the CA works as a distributed scheduler. In this phase, for any initial allocation of tasks in a multiprocessor system, the CA-based scheduler finds an allocation minimizing the total execution time of the program in a given system topology. The effectiveness of the proposed scheduling algorithm is shown for a number of program graphs scheduled in a two-processor system.  相似文献   

12.
This paper proposes a cellular automata-based solution of a binary classification problem. The proposed method is based on a two-dimensional, three-state cellular automaton (CA) with the von Neumann neighborhood. Since the number of possible CA rules (potential CA-based classifiers) is huge, searching efficient rules is conducted with use of a genetic algorithm (GA). Experiments show an excellent performance of discovered rules in solving the classification problem. The best found rules perform better than the heuristic CA rule designed by a human and also better than one of the most widely used statistical method: the k-nearest neighbors algorithm (k-NN). Experiments show that CAs rules can be successfully reused in the process of searching new rules.  相似文献   

13.
Cellular automata (CA) are able to produce a global behavior from local interactions between their units. They have been applied to the task scheduling problem in multiprocessor systems in a very distinguished way. As this problem is NP-Complete, heuristics and meta-heuristics are usually employed. However, these techniques must always start the scheduling process from scratch for each new parallel application given as input. On the other hand, the main advantage to use CA for scheduling is the discovery of rules while solving one application and their subsequent reuse in other instances. Recently studies related to CA-based scheduling have shown relevant approaches as the use of synchronous updating in CA evolution and good results in multiprocessor systems with two processors. However, some aspects, such as the low performance of CA-based schedulers in architectures with more than two processors and during the reuse of the discovered rules, need to be investigated. This paper presents two new models to improve CA-based scheduling to deal with such aspects. The first proposal refers to the employment of a construction heuristic to initialize CA evolution and the second one is a new neighborhood model able to capture the dependence and relations strength among the tasks in a very simple way. It was named pseudo-linear neighborhood. An extensive experimental evaluation was performed using graphs of parallel programs found in the literature and new ones randomly generated. Experimental analysis showed the combined application of both techniques makes the search for CA transition rules during learning stage more robust and leads to a significant gain when considering the reuse of them on real-world conditions.  相似文献   

14.
The paper presents cellular automata (CA)-based multiprocessor scheduling system, in which an extraction of knowledge about scheduling process occurs and this knowledge is used while solving new instances of the scheduling problem. There are three modes of the scheduler: learning, normal operating, and reusing. In the learning mode, a genetic algorithm is used to discover CA rules suitable for solving instances of a scheduling problem. In the normal operating mode, discovered rules are able to find automatically, without a calculation of a cost function, an optimal or suboptimal solution of the scheduling problem for any initial allocation of program tasks in a multiprocessor system. In the third mode, previously discovered rules are reused with support of an artificial immune system (AIS) to solve new instances of the problem. We present a number of experimental results showing the performance of the CA-based scheduler.  相似文献   

15.
16.
Cellular automata (CA) have shown to be a viable approach in ecological modelling, in particular when dealing with local interactions between species and their environment. In CA modelling complex patterns emerge on a global scale through the evolution of interactions at a local level. Although the validity of a cell-based approach has successfully been demonstrated in numerous cases, very few studies have been reported that address the effects of cell size and configuration on the behaviours of CA-based models. In this paper, the performance of a cellular automaton based prey–predator model (EcoCA) developed by the author was first calibrated against the classical Lotka–Volterra (LV) model. The model was then used to investigate effects of cell size and cellular configurations (viz. the ‘computational stencil’). By setting up systematic simulation scenarios it was observed that the choice of a particular cell size has a clear effect on the resulting spatial patterns, while different cellular configurations affect both spatial patterns and system stability. On the basis of these findings, it is proposed to use the principal spatial scale of the studied ecosystem as CA model cell size and to apply the Moore type cell configuration. Methods for identifying principal spatial scales have been developed and are presented here.  相似文献   

17.
This paper describes the application of cellular automata (CA) to various image processing tasks such as denoising and feature detection. Whereas our previous work mainly dealt with binary images, the current work operates on intensity images. The increased number of cell states (i.e. pixel intensities) leads to a vast increase in the number of possible rules. Therefore, a reduced intensity representation is used, leading to a three state CA that is more practical. In addition, a modified sequential floating forward search mechanism is developed in order to speed up the selection of good rule sets in the CA training stage. Results are compared with our previous method based on threshold decomposition, and are found to be generally superior. The results demonstrate that the CA is capable of being trained to perform many different tasks, and that the quality of these results is in many cases comparable or better than established specialised algorithms.  相似文献   

18.
根据数字图像的存储特点,提出一种基于扩展型二维元胞自动机的图像加密算法,将二维元胞自动机与图像加密技术结合,利用元胞自动机生成数值范围在0-255区间的二维伪随机数矩阵,截取与图像大小相等的伪随机数矩阵作为密码对图像像素进行加密,解密为加密的逆过程。实验结果表明,该算法能快速产生密码,加密形式简单,具有较好的抗攻击能力,适合对数据量大的数字图像进行加密。  相似文献   

19.
In the density classification problem, a binary cellular automaton (CA) should decide whether an initial configuration contains more 0s or more 1s. The answer is given when all cells of the CA agree on a given state. This problem is known for having no exact solution in the case of binary deterministic one-dimensional CA. We investigate how randomness in CA may help us solve the problem. We analyse the behaviour of stochastic CA rules that perform the density classification task. We show that describing stochastic rules as a “blend” of deterministic rules allows us to derive quantitative results on the classification time and the classification time of previously studied rules. We introduce a new rule whose effect is to spread defects and to wash them out. This stochastic rule solves the problem with an arbitrary precision, that is, its quality of classification can be made arbitrarily high, though at the price of an increase of the convergence time. We experimentally demonstrate that this rule exhibits good scaling properties and that it attains qualities of classification never reached so far.  相似文献   

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