Description: 遗传算法(GA)和神经网络(network)结合的算法，用于图像分类，包含完整的工程-Genetic Algorithm (GA) and neural network (network) combining algorithm, for image classification, including the complete works Platform: |
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Author:haoyubao |
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Description: 在Visual C++下实现聚类分析在图像识别中的应用。其中包括模糊聚类，遗传算法聚类分析，层次聚类和动态聚类等算法。可以运行，并有分类的源文件。是不错的研究素材。-In Visual C++ Achieve clustering analysis in image recognition applications. Including fuzzy clustering, genetic algorithm for cluster analysis, hierarchical clustering and dynamic clustering algorithm, etc.. Can run, and the classification of the source file. Is a good study material. Platform: |
Size: 229376 |
Author:dd |
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Description: 该程序通过遗传算法对图像进行聚类分析，并实现了图像的分类功能，分类效果明显较其他算法好-The genetic algorithm procedure for cluster analysis of the images and to achieve the classification of image features, the classification results significantly better than the other algorithms Platform: |
Size: 181248 |
Author:fanyanwei |
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Description: 基于L-M算法的BP神经网络分类器 对图像分类有较好的效果-LM algorithm based on BP neural network classifier for image classification has a good effect Platform: |
Size: 182272 |
Author:sds |
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Description: 提出一种基于支持向量机( SVM)和遗传算法(GA)的离散余弦变换(DCT)域盲数字图像水印方法. 该方法能自适应于图像的局部特征. 依据图像块的局部特性,利用SVM对图像块分类,自适应地确定水印嵌入强度, GA用来优化水印嵌入位置. 实验结果表明该方法有较好的不可见性和较强对抗攻击的鲁棒性.-Based on support vector machine (SVM) and genetic algorithm (GA), discrete cosine transform (DCT) domain blind digital image watermarking method. The method adaptive to local image features. Based on the local characteristics of image block using the SVM the image block classification, adaptive to determine the embedding strength, GA to optimize the watermark embedding location. The experimental results show that the method has good invisibility and robustness of the strong against the attacks. Platform: |
Size: 449536 |
Author:郭事业 |
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Description: 用于结合现有影像和新影像的多光谱图像处理工具的遗传算法，描述了基于遗传算法的监督分类等影像处理工具的程序实现。-For images and new images with the existing multi-spectral image processing tools, genetic algorithms, describes the classification based on genetic algorithms and other image processing tools to monitor the program implementation. Platform: |
Size: 481280 |
Author:吴艺 |
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Description: 分类是数据挖掘 、机器学习 和模式识别 中一个重要的研究领域。分类的目的是学会一个分类模型 (称作分类器)，该模型能把未知类别的数据项映射到给定类别中。目前发展较成熟的几种分类算法 如决策树、神经网络、贝叶斯方法、遗传算法等。分类具有广泛的应用，例如医学诊断、信用卡系统的信用分级、图像模式识别等。本毕业设计通过使用鸢尾属植物(IRIS)数据集，对当前数据挖掘中具有代表性的优秀分类算法进行分析和比较，总结出了各种算法的特性，为使用者选择算法或研究者改进算法提供了依据。-Classification of data mining, machine learning and pattern recognition is an important area of research. The purpose of classification is to learn a classification model (called classifier), the model can unknown types of data items mapped to a given category. Currently the more mature types of classification algorithms such as decision trees, neural networks, Bayesian methods, genetic algorithms. Classification with a wide range of applications such as medical diagnosis, credit card system, credit rating, image pattern recognition. The graduation project by using the Iris genus (IRIS) data sets, data mining has on the current outstanding representative of the analysis and comparison of classification algorithms, summed up the characteristics of various algorithms for researchers to improve the user selection algorithm or algorithm provided.
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Size: 580608 |
Author:江霞 |
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Description: 对SVM多类分类算法进行了研究，总结了不同分类算法的优缺点。接下来本文提出了基于GA(遗传算法)和KNN(K近邻)的SVM多类分类算法-The SVM multi-class classification algorithms studied, summed up the advantages and disadvantages of different classification algorithms. The next paper, based on GA (genetic algorithm) and KNN (K neighbors) of the SVM multi-class classification algorithm
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Author:zhangxiang |
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Description: 课程设计要求 0
实验一 基于模板匹配的字符识别 1
实验二 基于C均值算法的车辆检测与跟踪 3
实验三 水果分类系统设计 5
实验四 基于遗传算法的图像歪斜校准 6
实验五 基于BP网络的函数逼近 9
实验六 空调模糊控制系统设计 -Curriculum design requires 0
Experimental one based on template matching character recognition 1
C-means algorithm based on two experimental vehicle detection and tracking 3
In Experiment 3, 5 fruits and Classification System
Experiment 4 based on genetic algorithm for image skew calibration 6
Experiment 5 based on BP network function approximation 9
Experiment six air-conditioning fuzzy control system design 11 Platform: |
Size: 81920 |
Author:delen2096 |
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Description: 遗传算法+FCM 用于SAR图像分类问题中，讲图分为两类或者三类~~~~~~~~~。-Genetic algorithm is used to SAR+ FCM image classification problem, speak figure divided into two classes or three kinds of ~ ~ ~ ~ ~ ~ ~ ~ ~. Platform: |
Size: 6278144 |
Author:cheng |
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Description: 用于遥感图像分类处理的一个基于C++的遗传算法聚类程序-For remote sensing image classification based on genetic algorithm clustering procedure of the C++ Platform: |
Size: 182272 |
Author:邓刘昭芦 |
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Description: 对植物的分类研究已经突破了单纯从植物细胞及化学遗传成分的角度去鉴 定植物种类的方法, 可以综合应用图像处理技术和模式识别技术 , 辅以图像获取设备实现对植物的快速识别 。 为 此 , 精心选取了植物叶片图像的典型形状特征 , 构成了叶片识别的特征向量, 然后用概率神经网络 (P NN)作为分类 器 , 对样本进行训练。 实验结果证明 , 针对少量常见的植物叶片图像, PNN与 BP神经网络相比有更好的识别效率 -Research on the classification of the plant has been broke through the simple the Angle of plant cell and chemical genetic component to guide The method of plant species, can be integrated application of image processing and pattern recognition technology, supplemented by image acquisition device to realize fast recognition of plants.For this, carefully the typical shape characteristics of the plant leaf image, form the leaf recognition feature vector, then using probabilistic neural network (NN) P as a classifier, the training samples.The experimental results show that for a few common plant leaf image, PNN compared with BP neural network has better recognition efficiency Platform: |
Size: 300032 |
Author:hahah |
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Description: 随着计算机技术的飞速发展 , 对植物的分类研究已经突破了单纯从植物细胞及化学遗传成分的角度去鉴 定植物种类的方法, 可以综合应用图像处理技术和模式识别技术 , 辅以图像获取设备实现对植物的快速识别 。 为 此 , 精心选取了植物叶片图像的典型形状特征 , 构成了叶片识别的特征向量, 然后用概率神经网络 (P NN)作为分类 器 , 对样本进行训练。 实验结果证明 , 针对少量常见的植物叶片图像, PNN与 BP神经网络相比有更好的识别效率 。-With the rapid development of computer technology, the research on the classification of the plant has been broke through the simple the Angle of plant cell and chemical genetic component to guide The method of plant species, can be integrated application of image processing and pattern recognition technology, supplemented by image acquisition device to realize fast recognition of plants.For this, carefully the typical shape characteristics of the plant leaf image, form the leaf recognition feature vector, then using probabilistic neural network (NN) P as a classifier, the training samples.The experimental results show that for a few common plant leaf image, PNN compared with BP neural network has better recognition efficiency Platform: |
Size: 300032 |
Author:blwang |
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Search - genetic image classification