欢迎您！东篱公司
退出
-
申报数据库
-
立项数据库
-
成果数据库
-
项目获奖数据库
中文摘要：
为了减少均值偏移算法的计算量，提出一种基于预测模型的均值偏移加速算法．根据迭代序列不同的收敛特点，建立收敛预测模型，通过减少每次迭代时矢量离收敛点的距离来实现加速．从理论上证明了其收敛速度比原均值偏移算法快，实验结果也进一步表明，该算法明显地提高了收敛速度，同时可以保证跟踪的准确性．
英文摘要：
In order to reduce computational load of mean shift algorithm, an accelerated mean shift algorithm based on prediction model is proposed. Convergence prediction model is built according to the different convergence properties of the iterative sequences, and acceleration is realized by reducing the distance between the vector and convergence point at each iteration. It is proved theoretically that convergence speed of our algorithm is quicker than the original one. The experimental results also show that the convergence speed is improved greatly and the tracking accuracy is guaranteed.
同期刊论文项目
同项目期刊论文
A spatial orthogonal allocation and heterogeneous cultural hybrid algorithm for multi-robot explorat
Vision-based long-distance lane perception and front vehicle location for full autonomous vehicles o
A regularity model-based multiobjective estimation of distribution algorithm with reducing redundant
Fault detection and identification for dead reckoning system of mobile robot based on fuzzy logic pa
Hybridizing particle swarm optimization with differential evolution for constrained numerical and en
期刊信息
位置：