Description: 提出一种基于分形理论和BP 神经网络的航空遥感图像有监督分类方法。该方法尝试将航空图像
的光谱信息和纹理特征相结合。它首先将彩色航空图像由RGB 格式转化为HSI 格式,然后,根据亮度计算分
数维、多重分形广义维数谱q-D( q) 和“空隙”等基于分形的纹理特征,同时加入归一化的色度和饱和度作为光
谱特征,采用BP 神经网络作为分类器。通过对彩色航空图像的分类实验,结果证实该方法行之有效。-Based on fractal theory and BP neural network of aviation remote sensing image supervised classification method. This method tries to aerial images of the spectral information and texture characteristics of the combination. It will first color aerial images from the RGB format into HSI format, and then, according to the brightness calculation of fractal dimension, the generalized multi-fractal dimension spectrum qD (q) and the Platform: |
Size: 274432 |
Author:xuhuoping |
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Description: 利用BP神经网络进行图像分割。主要适用于RGB信息丰富的图像。以RGB为BP网络的三个输入，与对应的灰度图对网络进行训练。-The use of BP neural network image segmentation. RGB is mainly applied to information-rich images. BP network to RGB for the three inputs, with grayscale corresponds to the network training. Platform: |
Size: 24576 |
Author:血狼 |
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Description: 对机器视觉系统采集
的柑橘图像进行图像裁切、RGB 空间至HSI 空间的转换和差值法去图像背景，用色调H 和饱和度S 为输入，建立小波神经网络柑橘pH 预测模型，无损检测柑橘pH。-Images of citrus fruits from machine vision system were processed by
cutting, converting from RGB space to HSI space, removing background by deviation. A wavelet neural network model was constructed to detect pH value of citrus fruits non-destructively, the inputs of the model were image hue H and saturation S. Platform: |
Size: 1221632 |
Author:ygliang30 |
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Description: In this post, we are going to share with you, the MATLAB implementation of Color Quantization and Color Reduction of images, using intelligent clustering approaches: (a) k-Means Algorithm, (b) Fuzzy c-Means Clustering (FCM), and (c) Self-Organizing Map Neural Network. The implemented code, uses RGB and HSV color coding, to perform the clustering task, and user can desired approach of coding.-In this post, we are going to share with you, the MATLAB implementation of Color Quantization and Color Reduction of images, using intelligent clustering approaches: (a) k-Means Algorithm, (b) Fuzzy c-Means Clustering (FCM), and (c) Self-Organizing Map Neural Network. The implemented code, uses RGB and HSV color coding, to perform the clustering task, and user can desired approach of coding. Platform: |
Size: 252928 |
Author:Mustafa |
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Description: Color Reduction and Quantization using k-Means, Fuzzy Clustering (FCM), and SOM Neuarl Network in MATLAB
In this post, we are going to share with you, the MATLAB implementation of Color Quantization and Color Reduction of images, using intelligent clustering approaches: (a) k-Means Algorithm, (b) Fuzzy c-Means Clustering (FCM), and (c) Self-Organizing Map Neural Network. The implemented code, uses RGB and HSV color coding, to perform the clustering task, and user can select desired approach of coding. Platform: |
Size: 248832 |
Author:amardz |
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Description: 基于AlexNet网络模型的单幅彩色图的深度估计，在NYU Depth 数据集，Make3D 数据集，KITTI 数据集经过测试效果很好，只是本次上传由于大小限制，压缩包不包括数据集，读者可自行下载数据集进行训练！(Based on the AlexNet network model, the depth estimation of a single color map, in the NYU Depth dataset, Make3D dataset, KITTI dataset has been tested very well, but this upload due to size limitations, the compressed package does not include the dataset, the reader can Download the data set for training!) Platform: |
Size: 5120 |
Author:熊猫娃娃 |
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Description: 该课题为基于MATLAB bp神经网络的雾霾天气下交通标志的识别系统。主要分两步骤，一是进行图像去雾，采用暗通道的方法获取光透射率，从而去除雾霾。得到清晰的图片后，利用颜色的方法进行交通标志的定位，众所周知，交通标志基本是红，蓝，黄三色组成，根据RGB不同组合可以定位到不同颜色，因为存在误差，所以需要借助形态学相关知识，将得到的误干扰面积去除，从而实现精准定位。定位后，在原图基础上进行分割出彩色图标，利用bp神经网络方法，进行训练，识别，从而得出结果。本设计配有一个GUI可视化界面，操作简单容易上手。是个不错的选题。(This project is a traffic sign recognition system based on Matlab bp neural network in haze weather. There are two steps. One is image defogging, and the dark channel method is used to obtain light transmittance to remove haze. After getting clear pictures, use color method to locate traffic signs. As we all know, traffic signs are basically composed of red, blue and yellow. According to different combinations of RGB, different colors can be located. Because there are errors, we need to use morphological knowledge to remove the error interference area, so as to achieve accurate positioning. After positioning, the color icon is segmented on the basis of the original image. The BP neural network method is used to train and identify the color icon, and the result is obtained. This design is equipped with a GUI visual interface, which is easy to operate. It's a good topic.) Platform: |
Size: 83507200 |
Author:可乐一生 |
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