Description: 基于人工神经网络的图像识别方法研究。基于神经网络的人脸检测研究。基于特征融合与神经网络的手写体数字识别技基于遗传神经网络的手写体数字识别研究术研究。基于遗传优化的神经网络的银行票据手写数字识别。一种改进的人工神经网络模型-Based on artificial neural network image recognition method. Neural Network Based Face Detection Research. Based on Feature Fusion and Neural Network Recognition of Handwritten Numerals Based on Genetic Neural Network Technology of handwritten numeral recognition technique research study. Optimization based on genetic neural network bank notes handwritten numeral recognition. An Improved Artificial Neural Network Model Platform: |
Size: 13884416 |
Author:cheng |
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Description: 火灾视觉特征的提取是视觉火灾探测中的关键问题. 我们主要研究色彩、纹理以及轮廓脉动
等特征的提取,并提出一种度量轮廓脉动信息的距离模型,该模型在规格化的傅立叶描述子空间能
够准确地度量这种时空闪烁特征. 实验结果表明,该方法具有比较好的鲁棒性,有助于提高视觉火
灾探测的准确率、降低误报漏报率.-Based on investigating color , text ure and temporal feat ures for vision based fire detection , a
distance model of contour fluct uation between two successive f rames in t he normalized Fourier descriptor s
domain was presented to measure t his time varying contour fluct uation feat ure of flame. The model of
contour fluct uation is effective and robust for fire recognition. To f urt her reduce fal se alarms , several
features ext racted according to color , text ure and the distance model were toget her regarded as a joint
feature vector for artificial neural network to detect fire. Experiment s show t hat the algorithm is effective
and robust , and t hat it is significant for improving accuracy and reducing fal se alarms. Platform: |
Size: 819200 |
Author:陈卿 |
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Description: We propose an algorithm for facial expression recognition which can classify the given image into one of the seven basic facial expression categories (happiness, sadness, fear, surprise, anger, disgust and neutral). PCA is used for dimensionality reduction in input data while retaining those characteristics of the data set that contribute most to its variance, by keeping lower-order principal components and ignoring higher-order ones. Such low-order components contain the "most important" aspects of the data. The extracted feature vectors in the reduced space are used to train the supervised Neural Network classifier. This approach results extremely powerful because it does not require the detection of any reference point or node grid. The proposed method is fast and can be used for real-time applications. Platform: |
Size: 21504 |
Author:mhm |
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Description: 对拍摄得到的驾驶员视频帧图像, 使用复合肤色模型检测人脸 通过自适应边缘检测、 图像增强等方法处理得到
特征图像, 经特征区域筛选, 依据人脸先验知识匹配得到最佳人眼对 提取眼部特征向量, 结合 LVQ神经网络进行模式
识别检测眼部状态, 为判断驾驶员是否处于疲劳状态提供判据。-Video shot by the driver of the frame, the use of composite skin model of face detection through adaptive edge detection, image enhancement approach to be characteristic image, the feature area selection, based on prior knowledge of face matching the best human eye extracted eye feature vectors, combined with LVQ neural network pattern recognition detection of eye condition, to determine whether driver fatigue is provided in the criterion. Platform: |
Size: 106496 |
Author:廖减员 |
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Description: 使用Gabor特征提取和神经网络的人脸检测，里面带有人脸和非人脸的训练图库，检测效果很好。
运行该程序：
1 -所有文件和目录复制到MATLAB的工作文件夹 *-为了运行程序，你必须有图像处理和神经网络工具箱
2 - 找到名为“main.m”的文件
3 - 双击这个文件或在命令窗口中的“主”类型
4 - 将显示一个菜单。点击“火车网”，并等待，直到程序完成培训
5 - 点击“照片上的测试”。将出现一个对话框。选择一个。JPG图片
6 - 等待，直到程序检测到一些面孔
-Gabor feature extraction and neural network face detection, which with a human face and non-face training Gallery detection works well. Run the program: 1- all files and directories copied to MATLAB work folder*- In order to run the program, you must have image processing and neural network toolbox- find a file named " main.m" - double-click on the file or " master" in the command window type- will display a menu. Click on the train network, and wait until the process is complete training- Click the photo test. A dialog box appears. Select one. JPG picture- wait until the program detects some faces Platform: |
Size: 19137536 |
Author:朝颜 |
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Description: ace Detection Program for MATLAB 2013a
using Gabor Feature Extraction and Neural Networks
----------------------------------------------------------------
1- copy all files and directories to the MATLAB s work folder
* In order to run the program you must have Image Processing and Neural Networks Toolboxes
2- (Important) Navigate to the root folder which contains "main.m".
3- Type "main" or "run main" in the command window
4. Only fort the first time, the program creates
Gabor filters and stores them in ./data/gabor.mat
Training set dataset and stores it in ./data/imgdb.mat
Neural Network and stores it in ./data/net.mat
5- imgdb is short for "image data base".
6- When the program menu appears click on "Train Network" and wait until the program is
done with the training-ace Detection Program for MATLAB 2013a
using Gabor Feature Extraction and Neural Networks
----------------------------------------------------------------
1- copy all files and directories to the MATLAB s work folder
* In order to run the program you must have Image Processing and Neural Networks Toolboxes
2- (Important) Navigate to the root folder which contains "main.m".
3- Type "main" or "run main" in the command window
4. Only fort the first time, the program creates
Gabor filters and stores them in ./data/gabor.mat
Training set dataset and stores it in ./data/imgdb.mat
Neural Network and stores it in ./data/net.mat
5- imgdb is short for "image data base".
6- When the program menu appears click on "Train Network" and wait until the program is
done with the training Platform: |
Size: 180224 |
Author:manu |
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Description: 基于Gabor特征提取和神经网络的人脸检测的matlab程序-Human face detection based on Gabor feature extraction and neural network in matlab Platform: |
Size: 152576 |
Author:mark |
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Description: Face is a primary focus of attention in social intercourse, playing a major role in conveying identity and
emotion. The human ability to recognize faces is remarkable. People can recognize thousands of faces learned throughout
their lifetime and identify familiar faces at a glance even after years of separation. This skill is quite robust, despite large
changes in the visual stimulus due to viewing conditions, expression, aging, and distractions such as glasses, beards or
changes in hair style. In this work, a system is designed to recognize human faces depending on their facial features. Also
to reveal the outline of the face, eyes and nose, edge detection technique has been used. Facial features are extracted in the
form of distance between important feature points. After normalization, these feature vectors are learned by artificial
neural network and used to recognize facial image. Platform: |
Size: 240640 |
Author:fatemeh |
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Description: 主要是基于mtlab的程序，基于人工神经网络的常用数字信号调制，用于信号特征提取、信号消噪，毕业设计有用，有借鉴意义哦，含噪脉冲信号进行相关检测,采用加权网络中节点强度和权重都是幂率分布的模型。- Mainly based on the mtlab procedures, The commonly used digital signal modulation based on artificial neural network, For feature extraction, signal de-noising, Graduation useful There are reference Oh, Noisy pulse correlation detection signal, Using weighted model nodes in the network strength and weight are power law distribution. Platform: |
Size: 5120 |
Author:dytsvfrv |
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Description: 使用拉亚普诺夫指数的公式，在MATLAB中求图像纹理特征，是一种双隐层反向传播神经网络，现代信号处理中谱估计在matlab中的使用，通过虚拟阵元进行DOA估计，含噪脉冲信号进行相关检测,有小波分析的盲信号处理，D-S证据理论数据融合。- Raya Punuo Fu index using the formula, In the MATLAB image texture feature, Is a two hidden layer back propagation neural network, Modern signal processing used in the spectral estimation in matlab, Conducted through virtual array DOA estimation, Noisy pulse correlation detection signal, There Wavelet Analysis Blind Signal Processing, D-S evidence theory data fusion. Platform: |
Size: 5120 |
Author:gzfcg |
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Description: 为保障安全与电力用户供电质量，基于并网逆变器的分布式发电(distributed generation DG)系统要求具备孤岛检测功能。针对被动式孤岛检测法检测盲区(non-detectionzone，NDZ)大、检测时间长以及主动式孤岛检测法影响分布式发电系统供电质量的缺点，提出了一种新的被动式孤岛检测方法。该方法利用小波变换从公共耦合点(point ofcommon coupling，PCC)处的电压信号及逆变器输出电流信号中提取特征量，再通过BP 神经网络进行模式识别来判断是否出现孤岛现象。仿真与实验结果表明，该方法比传统的被动式孤岛检测方法检测速度快，检测盲区小。同时，由于所提供的孤岛检测法没有向控制信号中加入扰动量，因而不会对电能质量产生不良影响，克服了主动式孤岛检测方法的不足，并具有很高的准确性与可靠性。-The function of islanding detection is required for the grid-connected inverter-based distributed generation system due to safety reasons and to maintain the quality of power supply. Passive methods have a large non detection zoneand the detecting time is long, while active schemes have negative influence on power quality, so a novel passive islanding detection method was proposed. In this method, wavelet transform was adopted to extract feature vectors the voltage of point of common coupling (PCC) point and the output current of inverter, and then pattern recognition was exerted by BP neural network to determine whether there was an island phenomenon. The simulation and experiment results show that this method is faster than the traditional passive methods in islanding detection, and the non-detection zone is smaller. At the same time, because no disturbance was added to the control signal in the method, there isn’t a negative impact on power quality. The method overcomes the shortco Platform: |
Size: 609280 |
Author:kiel |
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Description: 为保障安全与电力用户供电质量，基于并网逆变器的分布式发电(distributed generation DG)系统要求具备孤岛检测功能。针对被动式孤岛检测法检测盲区(non-detectionzone，NDZ)大、检测时间长以及主动式孤岛检测法影响分布式发电系统供电质量的缺点，提出了一种新的被动式孤岛检测方法。该方法利用小波变换从公共耦合点(point ofcommon coupling，PCC)处的电压信号及逆变器输出电流信号中提取特征量，再通过BP 神经网络进行模式识别来判断是否出现孤岛现象。仿真与实验结果表明，该方法比传统的被动式孤岛检测方法检测速度快，检测盲区小。同时，由于所提供的孤岛检测法没有向控制信号中加入扰动量，因而不会对电能质量产生不良影响，克服了主动式孤岛检测方法的不足，并具有很高的准确性与可靠性。-The function of islanding detection is required for the grid-connected inverter-based distributed generation system due to safety reasons and to maintain the quality of power supply. Passive methods have a large non detection zoneand the detecting time is long, while active schemes have negative influence on power quality, so a novel passive islanding detection method was proposed. In this method, wavelet transform was adopted to extract feature vectors the voltage of point of common coupling (PCC) point and the output current of inverter, and then pattern recognition was exerted by BP neural network to determine whether there was an island phenomenon. The simulation and experiment results show that this method is faster than the traditional passive methods in islanding detection, and the non-detection zone is smaller. At the same time, because no disturbance was added to the control signal in the method, there isn’t a negative impact on power quality. The method overcomes the shortco Platform: |
Size: 609280 |
Author:kiel |
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Description: 用市面上的摄像头，可以实现实时人脸识别功能。(The algorithm model of facenet face recognition is obtained through deep learning, and the backbone network of feature extraction is concept-resnetv1, which is developed from concept network and RESNET, with more channels and network layers, so that each layer can learn more features and greatly improve the generalization ability. The network is deeper, the amount of calculation in each layer is reduced, and the ability of feature extraction is strengthened, so as to improve the accuracy of target classification. On the LFW data set, the accuracy of face recognition reaches 98.40%. In this experiment, mtcnn is introduced into the face detection algorithm. Its backbone network is divided into three convolutional neural networks: p-net, R-Net and o-net. Among them, o-net is the most strict in screening candidate face frames. It will output the coordinates of a human face detection frame and five facial feature points (left eye, right eye, nose, left mouth corner, right mouth corner).) Platform: |
Size: 2415616 |
Author:莱尼 |
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Search - feature detection in neural network