Description: matlab图形图像处理教程2，我用过，很不错的资料-matlab image processing tutorial 2 graphics, I used, very good information Platform: |
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Author:liuyongkang |
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Description: the book is very important for applying nn in image processing and fuzzy implementation
using c Platform: |
Size: 843776 |
Author:MOFREH |
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Description: 这是国外学者编的图像处理小波工具箱，十分好用。-This is a compilation of foreign scholars, image processing wavelet toolbox is very easy to use. Platform: |
Size: 54272 |
Author:zq |
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Description: 选定一张N×N的车牌彩色图像，进行256级的灰度处理。车牌号码识别。-Selected a N x N license plate color image gray processing, 256. Vehicle license plate recognition.
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Author: |
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Description: 基于MATLAB实现了极坐标下的傅里叶变换，对一个给定 n×n 的二维信号，其计算复杂度等价于笛卡尔坐标下的2D-FFT，可广泛应用于图像处理和分析。-A fast high accuracy Polar FFT algorithm is given in the software package written in MATLAB. For a given 2D signal of size n×n, the algorihtm s complexity is just like in a Cartesian 2D-FFT, which can be used widely in image processing and analysis. Platform: |
Size: 579584 |
Author:周清保 |
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Description: 神经网络训练算法，加速算法，有很大改进，建立参考。-Recent years have seen a surge of interest in multilayer neural networks fueled by their successful applications in numerous image processing and computer vision tasks. In this article, we describe a C++ implementation of the stochastic gradient descent to train a multilayer neural network, where a fast and accurate acceleration of tanh(·) is achieved with linear interpolation. As an example of application, we present a neural network able to deliver state-of-the-art performance in image demosaicing Platform: |
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Author:XXXXXX123 |
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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 - NN image processing