为降低特征点配准的计算量，提出了一种聚类凸集投影算法。
A clustering successive projection onto convex sets algorithm is presented for fast point matching.
它可以看作是序贯凸集投影算法结合聚类思想而得到的推广。
The resulting algorithm can be viewed as an extention of SPOCS by combining with clustering.
为了改善超分辨率重建图像的效果，提出了一种基于线过程模型的凸集投影方法。
In order to improve the superresolution reconstruction of image, a method of projections onto convex sets (POCS) based on the line process modeling is proposed.
结合过完备小波变换和凸集投影集(POCS)算法，提出了一种对信道差错鲁棒的多描述编码算法。
An error robust multiple description coding algorithm was proposed, which combines the overcomplete wavelet transform and projection onto convex sets (POCS).
本文基于文献[3]中的利用凸集投影(POCS)理论的去方块方法，提出一种改进方案来降低图像的方块效应。
In this paper, we propose an improving method of removing the blocking artifacts on images based on the theory of projections onto convex sets (POCS) in 3 to decrease the blocking effect.
针对基于能量的分布式目标定位算法——加权凸集投影法（pocs）精度不高、稳定性差的不足，提出一种加权POCS算法。
This paper proposes a distributed object oriented algorithm based on energy weighted Projections onto Convex Sets(POCS) to solve the problem that POCS is not accurate and stable.
通过迭代求解法和高斯金字塔模型，快速精确地估计得到配准参数，采用凸集投影（POCS）算法对图像序列进行了超分辨率重建。
Based on the set theoretic formulation, a projection onto convex sets (POCS) algorithm is applied to find the solution to face image reconstruction.
通过迭代求解法和高斯金字塔模型，快速精确地估计得到配准参数，采用凸集投影（POCS）算法对图像序列进行了超分辨率重建。
Based on the set theoretic formulation, a projection onto convex sets (POCS) algorithm is applied to find the solution to face image reconstruction.
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