1) depth weighted normal map
深度加权法向映射
1.
In this paper, we propose a novel 3D model retrieval algorithm based on a new shape descriptor, “depth weighted normal map”.
提出一种基于深度加权法向映射的三维检索算法 ,从归一化处理后的物体形状出发 ,计算关于视点方向的深度加权表面法向统计分布 ,并将该分布沿视点方向作球面调和分析得到深度加权法向映射特征 ,通过比较该特征的距离来度量任意三维模型之间的相似
2) additive map
加法映射
1.
In this paper,the authors characterize the additive maps from H_n(D) into H_m(D) that preserve rank 1 in some addition.
本文刻画了某条件下从Hn(D)到Hm(D)保秩1的加法映射。
2.
Struction of additive maps preserving rank-1 matrices from S_n(C) to H_n(C) are characterized.
确定了从Sn(R)到Hn(C)保秩1的加法映射的结构。
3.
In this paper it is shown that if f :Mmn(F)→Mpq(F) is an additive map preserving rank-one matrices and satisfying rank(f (G) + f (H)) >1 for some .
若一个映射f:Mm(nF)→Mpq(F)满足f(M1mn(F))哿M1pq(F)且f(A+B)=f(A)+f(B),坌A,B∈Mmn(F),则称f是保持秩1矩阵的加法映射。
3) additive mappings
加法映射
1.
Rank-one nonincreasing additive mappings on symmetric matrices;
对称矩阵空间上秩1非增长的加法映射(英文)
2.
The authors characterize additive mappings ψ on V × V such that ψ(Qs) ? Qs for a ?xed s.
刻划了V上满足Ψ(Qs) ? Qs的加法映射Ψ。
4) normal mapping
法向映射
1.
By taking advantage of normal mapping, only the silhouette to current viewpoint is refined and transmitted, and consequently the amount of patches needed to transfer each frame is greatly reduced.
该算法利用法向映射提高传输和绘制的效率,然后随着视点的变化动态细化和传输当前视点下轮廓部分的参数面片信息,从而最大限度地减少了模型传输时面片的数量。
5) depth-weighted
深度加权
1.
In order to avoid this problem,a new nonparametric regression model,called the depth-weighted regres- sion model,is introduced and then the depth-weighted wavelet estimation is defined.
在非参数回归模型中,原有的回归估计对于数据的扰动不稳健,为避免此问题,文章给出一种称为深度加权回归模型的新的非参数回归模型,并且定义了一种深度加权小波估计,这种新的估计对于数据的扰动是稳健的,有非常高的崩溃值,其值接近1/2。
2.
A class of projection-based depth functions,depth-weighted mean and depth-weighted LSE were studied.
本文介绍了一种投影深度函数,深度加权平均和深度加权LSE,这些估计量有符合需要的稳健性。
6) Acceleration projection
加速度映射
补充资料：因侵害姓名权、肖像权、名誉权、荣誉权产生的索赔权
因侵害姓名权、肖像权、名誉权、荣誉权产生的索赔权：公民、法人的姓名权、名称权，名誉权、荣誉权、受到侵害的有权要求停止侵害，恢复名誉，消除影响，赔礼道歉，并可以要求赔偿损失。
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