搜索资源列表
Saliency-Detection
- 提出一种新的显着性检测方法,通过将区域级显着性估计和像素级显着性预测与CNN(表示为CRPSD)相结合。对于像素级显着性预测,通过修改VGGNet体系结构来执行完全卷积神经网络(称为像素级CNN)以执行多尺度特征学习,基于该学习进行图像到图像预测以完成像素级显着性检测。对于区域级显着性估计,首先设计基于自适应超像素的区域生成技术以将图像分割成区域,基于该区域通过使用CNN模型(称为区域级CNN)来估计区域级显着性。通过使用另一CNN(称为融合CNN)融合像素级和区域级显着性以形成nal显着图,并
ningbao
- 用于特征降维,特征融合,相关分析等,用MATLAB实现动态聚类或迭代自组织数据分析,包括 MUSIC算法,ESPRIT算法 ROOT-MUSIC算法。- For feature reduction, feature fusion, correlation analysis, Using MATLAB dynamic clustering or iterative self-organizing data analysis, Including the MUSIC algorithm, ESPRI
henglui
- 可实现对二维数据的聚类,高斯白噪声的生成程序,用于特征降维,特征融合,相关分析等。- Can realize the two-dimensional data clustering, Gaussian white noise generator, For feature reduction, feature fusion, correlation analysis.
sun_v53
- MIMO OFDM matlab仿真,用于特征降维,特征融合,相关分析等,数据模型归一化,模态振动。- MIMO OFDM matlab simulation, For feature reduction, feature fusion, correlation analysis, Normalized data model, modal vibration.
YUVOSDMixerTest
- yuv数据与文字叠加demo显示,用于YUV视频帧数据与文字的融合处理-YUV data and text overlay demo display for YUV video frame data and text fusion processing
buibao
- 用于特征降维,特征融合,相关分析等,用MATLAB实现动态聚类或迭代自组织数据分析,使用大量的有限元法求解偏微分方程。- For feature reduction, feature fusion, correlation analysis, Using MATLAB dynamic clustering or iterative self-organizing data analysis, Using a large number of finite element method to sol
estarfm_update_20140710
- 针对不同源高低空间分辨率的遥感数据,融合构建高时空分辨率的遥感数据-For different sources of high and low spatial resolution remote sensing data, high spatial and temporal resolution of the remote sensing data fusion
optical-flow-navigation
- 针对小型无人机在无卫星导航信号条件下的导航问题, 结合光流及地标定位设计了使用摄像头、惯性测量器件、超声测距仪等传感器融合的无人机室内导航方法. 文章使用补偿角速率的光流微分法计算帧间像素点小位移, 并用前后误差算法提取精度较高的点, 避免像素点跟踪错误, 提高了光流测速的精度 对得到的光流场用均值漂移算法进行寻优, 得到光流场直方图峰值, 以此计算光流速度. 本文提出了无累积误差的连续地标定位算法, 实时测量无人机位置. 通过多速率卡尔曼滤波器对观测周期不一致的位置、速度信息进行最优估计. 在
pao_uh30
- 迭代自组织数据分析,包括邓氏关联度、绝对关联度、斜率关联度、改进绝对关联度,用于特征降维,特征融合,相关分析等。- Iterative self-organizing data analysis, Including Deng s correlation, absolute correlation, correlation of slope, improved absolute correlation, For feature reduction, feature fusion, correla
mpu6050-hmc5883l
- 使用互补滤波实现对mpu6050、hmc5883l输出数据的融合,输出姿态,基于stm32开发。-Using complementary filtering to achieve mpu6050, hmc5883l output data fusion, output attitude, based on stm32 development.
2281
- 用于特征降维,特征融合,相关分析等,用MATLAB实现动态聚类或迭代自组织数据分析,BP神经网络的整个训练过程。- For feature reduction, feature fusion, correlation analysis, Us
jei_v55
- 用于特征降维,特征融合,相关分析等,基于matlab平台实现,isodata 迭代自组织的数据分析。- For feature reduction, feature fusion, correlation analysis, Based on matlab platform, Isodata iterative self-organizing data analysis.
mdwsk
- 可实现对二维数据的聚类,用于特征降维,特征融合,相关分析等,PLS部分最小二乘工具箱。- Can realize the two-dimensional data clustering, For feature reduction, feature fusion, correlation analysis, PLS PLS toolbox.
ywsyx
- 用于建立主成分分析模型,数据模型归一化,模态振动,用于特征降维,特征融合,相关分析等。- Principal component analysis model for establishing, Normalized data model, modal vibration, For feature reduction, feature fusion, correlation analysis.
decision
- DSmT理论中的决策规则函数源码,对于信息融合的应用有一定的参考价值,尤其对应于冲突性数据。(DSmT theory of decision rule function source code, for the application of information fusion has some reference value, especially in conflict with data.)
tools
- DSmT理论中的工具箱函数源码,包括超幂集的产生等等,对于信息融合的应用有一定的参考价值,尤其对应于冲突性数据。(DSmT theory toolbox function source code, including the generation of super power set, and so on, for the application of information fusion, there is a certain reference value, especially in co
guandaofangzhen
- 实现了基于惯性导航定位的matlab仿真,并结合无线定位数据进行融合输出(The MATLAB simulation based on inertial navigation and positioning is realized, and the output is fused with the wireless positioning data)
mpu6050
- 利用单片机读取mpu6050的数据。用加速度值输出的数据求出X和Y轴上的倾斜角;用陀螺仪输出的的角速度值积分出旋转角度。 然后融合加速度计与陀螺仪的数据,最后滤波获得更好的效果。(Use singlechip to read mpu6050 data. The angle of the X and Y axes is calculated by the output data of the acceleration value, and the rotation angle is integra
voronoi
- 异构网络的融合,5Ghz,随着移动数据量的急剧增长,授权频谱(coexistence between cell and wifi)
Greynet
- 数学模型,将GM灰度模型与BP神经网络进行融合,可用于对数据的预测(The mathematical model combines the GM gray model with the BP neural network, and can be used to predict the data)