基于稀疏低秩分解的杂草种子配准  

Weed seeds registration based on sparse and low-rank decomposition

作　　者：张明[1] 朱俊平[1] 蔡骋[1] 

机构地区：[1]西北农林科技大学信息工程学院,陕西杨凌712100

出　　处：《计算机工程与设计》2012年第10期3959-3963,共5页Computer Engineering and Design

基　　金：国家自然科学基金项目(60975007);陕西省自然科学基金项目(2010JQ8019);陕西省科技计划基金项目(2010K06-15)

摘　　要：针对杂草种子识别在实际应用中的困难,提出了一种适用于杂草种子配准的稀疏低秩分解算法。阐述了稀疏低秩算法的原理和求解方法,原本有等式约束且非凸的问题可以通过求解核范式和l1范式的无约束凸优化问题得到很好的配准结果。为了验证配准工作的重要性,运用k折交叉检验对比配准前后的识别率差异。实验结果表明,基于稀疏低秩分解的配准算法能够提高杂草种子的识别率,为实际中的杂草种子识别提供了可行方案。It is difficult to apply the weed seeds recognition method in real world. A sparse low-rank decomposition algorithm is proposed and introduced for weed seeds registration. The problem originally having equality constraints and being non-convex is obtained by solving the nuclear norm and l1 minimization problem with unconstrained condition. To verify the importance of registration, k-fold cross validation is used to compare the results. Experimental results indicate that the recognition rate based on this registration algorithm can improve the recognition rate. A practicable solution is established in the future.

关 键 词：图像配准 稀疏低秩分解 凸优化 核范式 雅克比矩阵 k折交叉检验检验 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程]