Description: 灰度共生矩阵和灰度梯度共生矩阵的提取方式，是比较重要的纹理特征提取方法，用matlab实现的-Gray Level Co-occurrence matrix and gray-gradient co-occurrence matrix extraction method is more important texture feature extraction method, using matlab realize the Platform: |
Size: 2048 |
Author:子羽 |
Hits:
Description: 灰度共生矩阵和灰度梯度共生矩阵的提取方式，是比较重要的纹理特征提取方法，用matlab实现的-Gray Level Co-occurrence matrix and gray-gradient co-occurrence matrix extraction method is more important texture feature extraction method, using matlab realize the Platform: |
Size: 2048 |
Author:明明 |
Hits:
Description: 边缘特征的提取就是求图像梯度的局部最大值和方向。实际计算中，以微分算子的形式表示，并采用快速卷积函数来实现。常用的算子有微分算子，拉普拉斯算子，Canny算子等。其中Canny边缘检测是一种较新的边缘检测算子，具有较好的边缘检测性能，得到越来越广泛的应用。Canny边缘检测法利用高斯函数的一阶微分，它能在噪声抑制和边缘检测之间取得较好的平衡-Edge feature extraction is to seek the local maximum of image gradient and orientation. The actual calculation to the form of differential operator representation, and using fast convolution function to achieve. Commonly used operators are differential operators, Laplace operator, Canny operator and so on. Canny edge detection which is a relatively new edge detection operator, and has good edge detection performance, get more and more widely used. Canny edge detection method using first derivative of Gaussian function, it can in the noise suppression and edge detection to achieve a better balance between Platform: |
Size: 1024 |
Author:xiaowei |
Hits:
Description: 特征提取中，梯度方向直方图hog的几篇文章，研究HOG的同仁们很有用-Feature extraction, the gradient direction histogram hog several articles, research colleagues are useful HOG Platform: |
Size: 7685120 |
Author:丁丁 |
Hits:
Description: 用matlab做的手势识别 基于梯度方向直方图特征提取 欧氏距离判断-Gesture recognition with matlab do the gradient direction histogram feature extraction based on Euclidean distance to determine Platform: |
Size: 3097600 |
Author:袁永金 |
Hits:
Description: hog特征提取，提取梯度直方图信息，为后续工作获取材料-hog feature extraction to extract gradient histogram information, and to obtain material for the follow-up work Platform: |
Size: 5120 |
Author:利比亚 |
Hits:
Description: 把 SIFT 算法应用在牙齿模型图像上，检测牙齿图像的特征点。 方法：首先采用高斯差分算子 DoG 搜索整个图
像的尺度和位置信息，从而确定具有代表性尺度、方向的特征点。基于其稳定性选择关键点，得到一个详细的模型以确定每个候
选点的合适位置和范围。基于局部图像梯度方向信息将方向矢量和关键点对应起来。在选定范围内的每个关键点周边区域测量
局部图像梯度，并采用 KNN 算法进行特征匹配。 结果：通过大量的实验和与其他特征提取方法相比较，该方法能有效地检测牙
齿模型图像的特征，并为牙齿模型三维重建提供有效的参数。-SIFT algorithm is applied to the teeth of the model image, the image feature point detecting teeth. Methods: DoG Gaussian differential operator to search the entire image the scale and location information, to determine a representative scale, the direction of the feature point. Select the key points based on their stability, to get a detailed model to determine the appropriateness of each candidate point location and extent. Information based on local image gradient direction and key points of the direction vectors correspond. Within the selected area around each critical point of measuring the local image gradient, and using KNN algorithm for feature matching. Results: Through a lot of experiments with other feature extraction methods and compare the proposed method can effectively detect tooth model image feature, and to provide an effective three-dimensional reconstruction tooth model parameters. Platform: |
Size: 991232 |
Author:焦婷 |
Hits:
Description: 为了实现复杂环境下的人脸特征有效表达，提出一种改进的梯度方向直方图（ＨＯＧ）人脸识别方法．首先以人脸图像网格作为采样窗口并在其上提取 ＨＯＧ特征；然后将所有网格 ＨＯＧ特征向量进行组合，实现整个人脸特
征表达；最后采用最近邻分类器进行识别．另外，比较了该方法与Ｇａｂｏｒ小波和局部二值模式（ＬＢＰ）２种著名的人脸
局部特征表示方法的优劣．实验结果表明，在调优的 ＨＯＧ参数下，在具有光照和时间环境等复杂变化的ＦＥＲＥＴ人
脸库中，较少维数的 ＨＯＧ特征比ＬＢＰ特征有更好的表现，而且 ＨＯＧ特征提取时间和特征向量维数比Ｇａｂｏｒ小波方法更具有优势-In order to achieve facial features in complex environments valid expression, an improved gradient direction histogram (HOG) face recognition method. Firstly face image and extract the grid as a sample window HOG features on it then all mesh HOG feature vector combination, realize the whole people express facial feature Finally, nearest neighbor classifier to identify. In addition, the comparison of the method with Gabor wavelet and local binary pattern (LBP) 2 famous facial features indicate the quality of the local approach. Experimental results show that HOG parameter tuning in FERET face with complex changes in the environment of light and time, the characteristic dimension of less than HOG LBP features better performance, and feature extraction time and HOG dimension of feature vectors have an advantage over Gabor wavelet method Platform: |
Size: 1274880 |
Author:wang |
Hits:
Description: This paper describe the features extraction algorithm for electrocardiogram (ECG) signal using Huang Hilbert Transform and Wavelet Transform. ECG signal for an individual human being is different due to unique heart structure. The purpose of feature extraction of ECG signal would allow successful abnormality detection and efficient prognosis due to heart disorder. Some major important features will be extracted ECG signals such as amplitude, duration, pre-gradient, post-gradient and so on. Therefore, we need a strong mathematical model to extract such useful parameter. Here an adaptive mathematical analysis model is Hilbert-Huang transform (HHT). This new approach, the Hilbert-Huang transform, is implemented to analyze the non-linear and nonstationary data. It is unique and different the existing methods of data analysis and does not require an a priori functional basis. The effectiveness of the proposed scheme is verified through the simulation.-This paper describe the features extraction algorithm for electrocardiogram (ECG) signal using Huang Hilbert Transform and Wavelet Transform. ECG signal for an individual human being is different due to unique heart structure. The purpose of feature extraction of ECG signal would allow successful abnormality detection and efficient prognosis due to heart disorder. Some major important features will be extracted ECG signals such as amplitude, duration, pre-gradient, post-gradient and so on. Therefore, we need a strong mathematical model to extract such useful parameter. Here an adaptive mathematical analysis model is Hilbert-Huang transform (HHT). This new approach, the Hilbert-Huang transform, is implemented to analyze the non-linear and nonstationary data. It is unique and different the existing methods of data analysis and does not require an a priori functional basis. The effectiveness of the proposed scheme is verified through the simulation. Platform: |
Size: 1024 |
Author:Manish |
Hits:
Description: This paper describe the features extraction algorithm for electrocardiogram (ECG) signal using Huang Hilbert Transform and Wavelet Transform. ECG signal for an individual human being is different due to unique heart structure. The purpose of feature extraction of ECG signal would allow successful abnormality detection and efficient prognosis due to heart disorder. Some major important features will be extracted ECG signals such as amplitude, duration, pre-gradient, post-gradient and so on. Therefore, we need a strong mathematical model to extract such useful parameter. Here an adaptive mathematical analysis model is Hilbert-Huang transform (HHT). This new approach, the Hilbert-Huang transform, is implemented to analyze the non-linear and nonstationary data. It is unique and different the existing methods of data analysis and does not require an a priori functional basis. The effectiveness of the proposed scheme is verified through the simulation.-This paper describe the features extraction algorithm for electrocardiogram (ECG) signal using Huang Hilbert Transform and Wavelet Transform. ECG signal for an individual human being is different due to unique heart structure. The purpose of feature extraction of ECG signal would allow successful abnormality detection and efficient prognosis due to heart disorder. Some major important features will be extracted ECG signals such as amplitude, duration, pre-gradient, post-gradient and so on. Therefore, we need a strong mathematical model to extract such useful parameter. Here an adaptive mathematical analysis model is Hilbert-Huang transform (HHT). This new approach, the Hilbert-Huang transform, is implemented to analyze the non-linear and nonstationary data. It is unique and different the existing methods of data analysis and does not require an a priori functional basis. The effectiveness of the proposed scheme is verified through the simulation. Platform: |
Size: 2048 |
Author:Manish |
Hits:
Description: 一种噪声辅助数据分析方法,基于分段非线性权重值的Pso算法，是一种双隐层反向传播神经网络，用于信号特征提取、信号消噪，利用自然梯度算法。- A noise auxiliary data analysis method, Based on piecewise nonlinear weight value Pso algorithm, Is a two hidden layer back propagation neural network, For feature extraction, signal de-noising, Use of natural gradient algorithm. Platform: |
Size: 5120 |
Author:eexxvi |
Hits:
Description: 是学习PCA特征提取的很好的学习资料，GPS和INS组合导航程序，阐述了负荷预测的应用研究,利用自然梯度算法，包括随机梯度算法，相对梯度算法。- Is a good learning materials to learn PCA feature extraction, GPS and INS navigation program, It describes the application of load forecasting, Use of natural gradient algorithm, Including stochastic gradient algorithm, the relative gradient algorithm. Platform: |
Size: 6144 |
Author:symipv |
Hits:
Description: 信号处理中的旋转不变子空间法，部分实现了追踪测速迭代松弛算法，是学习PCA特征提取的很好的学习资料，包括随机梯度算法，相对梯度算法，通过虚拟阵元进行DOA估计。- Signal Processing ESPRIT method, Partially achieved tracking speed iterative relaxation algorithm, Is a good learning materials to learn PCA feature extraction, Including stochastic gradient algorithm, the relative gradient algorithm, Conducted through virtual array DOA estimation. Platform: |
Size: 6144 |
Author:viyzkefg |
Hits:
Description: 阐述了负荷预测的应用研究,包括调制，解调，信噪比计算，包括随机梯度算法，相对梯度算法，用于信号特征提取、信号消噪，有PMUSIC 校正前和校正后的比较。-It describes the application of load forecasting, Includes the modulation, demodulation, signal to noise ratio calculation, Including stochastic gradient algorithm, the relative gradient algorithm, For feature extraction, signal de-noising, A relatively before correction and after correction PMUSIC. Platform: |
Size: 9216 |
Author:xcpwbkqbs |
Hits:
Search - gradient feature extraction