1) HSI transform
HSI变换
1.
Image fusion algorithm based on HSI transform and Quantum-Behaved Particle Swarm Optimization transform;
基于HSI变换和QPSO变换的图像融合算法
2.
An image fusion method based on HSI transform and Laplace decomposition is proposed for multi-focus micropaleon- tology image fusion.
针对多聚焦微体古生物图像的融合,提出了基于HSI变换和Laplace分解的融合方法。
2) HSI model
HSI模型
1.
R-value,G-value and B-value of rice color were determined through respective drawing of R-histogram,G-histogram and B-histogram,and then RGB model was converted to HSI model,so as to determine H-value,S-value an.
介绍了目前常用的两种颜色模式RGB模型与HSI模型在大米色泽检测中的应用。
2.
Color image segmentation based on HSI model owns dramatic independence,authenticity and division.
HSI模型的彩色图像分割方法具有传统的RGB彩色图像分割所不具有的出色的分量独立性和色彩的真实性,具备良好的易分割的特点。
3.
Based on realizing a self-timer system for the digital camera, this paper proposes a motion object detection method of single channel of HSI model based on background updating.
在实现数码照相机自拍功能的基础上,提出了一种基于HSI模型单通道背景更新的运动物体检测方法。
3) HSI model
HSI空间
1.
Real-time classification video retrieval system based on HSI model;
基于HSI空间的实时分级视频检索系统
4) HSI mode
HSI模式
5) HSI space
HSI空间
1.
A Gray image pseudo-color coding approach in HSI space based on image′s histogram;
基于直方图的HSI空间伪彩色编码研究
2.
The road scene image of RGB space was transformed into that of HSI space,a syncretic method of hue and saturation was used to extract the characteristic color area of sign in road scene,then traffic sign image was binarized,the noise was removed,and binarization target was pro.
将道路场景RGB空间图像转换为HSI空间,利用色调与饱和度融合的办法来提取道路场景中标志的特征颜色区域,二值化后排除噪声并通过投影法得到正确的标志区域,实现交通标志的定位。
3.
Firstly,the original image was transferred to HSI space,and the rough locations were detected by using the edge information of the characters and the operation of the mathematical morphology;and then,the accurate region of license plate was decided by the SOM using four features proposed by this paper.
首先,将原始图像转换到HSI空间上,利用图像的字符边缘特征信息和数学形态学操作对目标区域进行粗定位;然后,根据车牌固有的特征定义4种不同的特征值,通过自组织神经网络的训练,实现对车牌区域的精确定位。
6) HSI-color
HSI色彩
1.
Segmentation and Calculation of the Images of Blood Cells Based on HSI-color;
基于HSI色彩的血细胞图像的分割算法实现
补充资料：Radon变换和逆Radon变换
Radon变换和逆Radon变换
X线物理学术语。CT重建图像成像的主要理论依据之一。1917年澳大利亚数学家Radon首先论证了通过物体某一平面的投影重建物体该平面两维空间分布的公式。他的公式要求获得沿该平面所有可能的直线的全部投影(无限集合)。所获得的投影集称为Radon变换。由Radon变换进行重建图像的操作则称为逆Radon变换。Radon变换和逆Radon变换对CT成像的意义在于，它从数学原理上证实了通过物体某一断层层面“沿直线衰减分布的投影”重建该层面单位体积，即体素的线性衰减系数两维空间分布的可能性。
说明：补充资料仅用于学习参考，请勿用于其它任何用途。
参考词条