Description: 数据是来源于图像的不变矩提取，这里是用matlab实现的，后面可能需要用vc实现，不过也不一定，数据是由房房同学提供的，说不定不用做
利用最简单的bp神经网络来实现分类，一共两类车辆，这里是模拟实现把，识别效果还能用，先凑合着把，这里的特征提取也够玄乎。
-Data is derived from the image moment invariants extraction, this is achieved using matlab, behind vc may need to achieve, but not necessarily, the data is provided by atrial students might not need to do the simplest use of bp neural network to achieve the classification, a total of two types of vehicles, this is the realization of the simulation, also used to identify the effect, first make do with the, where feature extraction玄乎enough. Platform: |
Size: 1186816 |
Author:车林 |
Hits:
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 |
Hits:
Description: 基于BP神经网络的遥感图像分类代码。从样本中提取崇明岛东滩十种地物的光谱特征，并训练BP网络，再利用网络进行分类-BP neural network-based remote sensing image classification code. Extracted from samples of 10 kinds of Chongming Island, Dongtan features of the spectral characteristics and to train BP network, and then use the network to classify Platform: |
Size: 1413120 |
Author:李琛 |
Hits:
Description: matlab 神经网络算法应用于图片中人物性格识别,通过对神经网络的训练，可以识别图像中是男性还是女性-matlab neural network algorithm is applied to the picture of character recognition, neural network through training, can identify the image is male or female. . . Platform: |
Size: 638976 |
Author:Turing |
Hits:
Description: High information redundancy and correlation in face images result in efficiencies when such images are used directly for recognition. In this paper, discrete cosine transforms are used to reduce image information redundancy because only a subset of the transform coefficients are necessary to preserve the most important facial features such as hair outline, eyes and mouth. We demonstrate experimentally that when DCT coefficients are fed into a backpropagation neural network for classification, a high recognition rate can be achieved by using a very small proportion of transform coefficients. This makes DCT-based face recognition much faster than other approaches.-High information redundancy and correlation in face images result in inefficiencies when such images are used directly for recognition. In this paper, discrete cosine transforms are used to reduce image information redundancy because only a subset of the transform coefficients are necessary to preserve the most important facial features such as hair outline, eyes and mouth. We demonstrate experimentally that when DCT coefficients are fed into a backpropagation neural network for classification, a high recognition rate can be achieved by using a very small proportion of transform coefficients. This makes DCT-based face recognition much faster than other approaches. Platform: |
Size: 25600 |
Author:mhm |
Hits:
Description: Wavelet transforms are used to reduce image information redundancy because only a subset of the transform coefficients are necessary to preserve the most important facial features such as hair outline, eyes and mouth. We demonstrate experimentally that when Wavelet coefficients are fed into a backpropagation neural network for classification, a high recognition rate can be achieved by using a very small proportion of transform coefficients. This makes Wavelet-based face recognition much more accurate than other approaches.
Platform: |
Size: 21504 |
Author:mhm |
Hits:
Description: 运用神经网络对26个大写的英文字母进行识别，对英文字母进行一定的图像处理，之后用神经网络进行分类-The use of neural network 26 uppercase letters of the alphabet recognition, the letters of the alphabet for a certain image processing, followed by neural network classification Platform: |
Size: 1024 |
Author:田丹 |
Hits:
Description: 运用神经网络对26个大写的英文字母进行识别，对英文字母进行一定的图像处理，之后用神经网络进行分类-The use of neural network 26 uppercase letters of the alphabet recognition, the letters of the alphabet for a certain image processing, followed by neural network classification Platform: |
Size: 1024 |
Author:田丹 |
Hits:
Description: 运用神经网络对26个大写的英文字母进行识别，对英文字母进行一定的图像处理，之后用神经网络进行分类-The use of neural network 26 uppercase letters of the alphabet recognition, the letters of the alphabet for a certain image processing, followed by neural network classification Platform: |
Size: 1024 |
Author:田丹 |
Hits:
Description: 为实现合格和缺陷板栗的分级， 研究了 1 种基于 BP 神经网络与板栗图像特征的板栗分级方法。 试验以罗田板
栗为研究对象， 提取的颜色及纹理等 8 个特征值， 通过主成分分析提取相应的主成分得分向量构成模式识别的输入。 利
用 BP 神经网络方法建立了板栗分级模型。 试验结果表明， 在图像信息主成分因子数为 3， 中间层节点数为 12 时， 建立
的模型最佳， 模型训练时的回判率为 100 ， 预测时识别率达到了 91 .67 。 研究结果表明基于机器视觉技术的针对缺陷
板栗分级检测方法是可行的。- In order to realize grading of eligible and defected chestnut by using machine vision, a classification method
of chestnut was developed based on BP-ANN and image feature of chestnut. In this experiment, Luotian chestnuts were
used as experimental targets. Principal component analysis (PCA) was implemented on these feature variables from
eight eigen values including color parameters and veins characteristics parameters etc., and principal components (PCs)
vectors were extracted as the inputs of pattern recognition. Grading models were built by BP neural network. The test
result showed that when the number of principal component factor was three and the number of nodes of hidden layer
was twelve, the discriminating rate was as high as 100 in training set, and 91.67 in prediction set. The overall results
shows that it is feasible to discriminate chestnut quality with machine vision. Platform: |
Size: 1015808 |
Author:李祥龙 |
Hits:
Description: 神经网络引入后，检测框架变得更快更准确。然而，大多数检测方法受限于少量物体。检测和训练数据上联合训练物体检测器，用有标签的检测图像来学习精确定位，同时用分类图像来增加词汇和鲁棒性。原YOLO系统上生成YOLOv2检测器；在ImageNet中超过9000类的数据和COCO的检测数据上，合并数据集和联合训练YOLO9-After the neural network is introduced, it is becoming faster and more accurate detection frame. However, most detection methods is limited by the small number of objects. Testing and training on joint training data object detector for detecting an image tag to learn precise positioning, while using image classification to increase vocabulary and robustness. YOLOv2 detector generates the original YOLO system ImageNet more than in the 9000 class of data and test data COCO, the consolidated data sets and joint training YOLO9000 Platform: |
Size: 2150400 |
Author:安宁 |
Hits:
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 |
Hits:
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 |
Hits:
Description: 图片情感分析模型，基于卷积神经网络，以颜色特征为依据进行情感分类，图片情感极性分为积极和消极两类。(The model can extract the hue, brightness, contrast and other information from a picture to represent the emotional polarity of the image. The image sentiment analysis model is using convolution neural network which is ideal for processing image data. By weighting this information fusion, we can get the emotional polarity of each pixel of the image. It can greatly improve accuracy rate of the image emotional classification model.) Platform: |
Size: 52354048 |
Author:安树声
|
Hits:
Description: Paper on : Classification of Forest Vertical Structure in
South Korea from Aerial Orthophoto and
Lidar Data Using an Artificial Neural Network , By
Soo-Kyung Kwon, Hyung-Sup Jung * ID , Won-Kyung Baek and Daeseong Kim Platform: |
Size: 5520384 |
Author:razibaz |
Hits:
Search - classification of image by neural network