1) gene image
基因图像
2) gene imaging
基因显像
1.
Advance in HSV1-tk gene for tumor report gene imaging and gene therapy;
HSV1-tk基因在肿瘤报告基因显像与治疗中的研究进展
2.
Advances in study of reporter gene imaging for monitoring gene therapy;
报告基因显像监测基因治疗研究进展
3.
With the development of gene research,the PET technique has played more and more important role in the gene imaging.
随着基因研究的发展,PET技术在基因显像中发挥着越来越重要的作用。
4) image-based
基于图像
1.
Fast face detection combine both feature-based and image-based approach;
基于特征和基于图像相结合的快速人脸检测
2.
In this paper, we describe an image-based rendering technique called Inverse Concentric Mosaics (ICM) which is based on Concentric Mosaics (CM) .
阐述了“逆向同心拼图”技术 ( inverse concentric mosaics,简称 ICM) ,ICM是一种基于图像的绘制技术 ,是“同心拼图”技术 ( concentric mosaics,简称 CM)的一种扩展。
3.
In recent years, we can seen clearly the trend of increasing number of researchers\' interest in image-based modeling technology for its advantages of low-cost, relatively fast speed of modeling.
近年来,基于图像的建模技术以其成本低、建模速度相对较快得到越来越多研究者的关注,从单幅图像建模更因其简单、方便处理等优点得到较快的发展。
5) basic image
基本图像
1.
A new fast two dimension 8×8 Inverse Discrete Cosine Transform(IDCT) algorithm based on Look-Up Table(LUT) is presented,in which,the LUT structure is based on the DCT basic images.
提出一种基于查表法的二维8×8离散余弦逆变换(2D 8×8 IDCT)的快速算法,其查找表LUT(Look-UpTable)结构的设计是基于二维8×8 DCT的基本图像。
2.
The specialities of the basic images of the discrete cosine transform (DCT) and the characteristics of the data of the practical images are discussed, and a fast algorithm for computing 8×8 two-dimensional IDCT is presented in this paper.
讨论了离散余弦变换(DCT)的基本图像的特性以及实际图像数据的特点,提出了利用基本图像进行二维8×8离散余弦逆变换(IDCT)的快速算法。
3.
The new algorithm is based on the conception and symmetry of basic images.
基于基本图像的概念及其对称性,提出一种计算二维8×8离散余弦变换(DCT)量化后系数的查表快速算法。
6) basic image
基图像
1.
Then the basic images of template image are presented and an image is expressed with the linear combination of the basic images.
首先证明了图像的大量信息主要体现在图像矩阵奇异值分解的前几个最大奇异值所对应的左、右奇异向量中,然后给出了模板图像的基图像,并将图像展开成基图像的线性表示,提取其组合系数作为图像的代数特征并用于人脸识别中。
2.
NMF is applied to the each group’s images to obtain the non-negative subspace constructed by basic image of each group’s images,project the training images and testing images to each feature subspace.
将训练图像分组,分别对每组图像作NMF,获取每组图像的基图像构成的非负特征子空间,将训练图像和测试图像分别向各个特征子空间进行投影,将每组图像提取出的特征系数混合,根据最近邻原则进行识别。
补充资料：高光谱分辨率遥感图像及图像光谱信息提取
高光谱分辨率遥感图像及图像光谱信息提取
高光谱分辨率遥感图像及图像光谱信息提取 郑兰芬供稿
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