English

应用科学学报 ›› 2013, Vol. 31 ›› Issue (6): 601-606.doi: 10.3969/j.issn.0255-8297.2013.06.008

• 信号与信息处理 • 上一篇    下一篇

全局运动中结合改进C-1BT 变换的平移运动估计

席志红， 初守艳   

  1. 哈尔滨工程大学信息与通信工程学院，哈尔滨150001
  • 收稿日期:2011-10-31 修回日期:2012-05-21 出版日期:2013-11-29 发布日期:2012-05-21
  • 作者简介:席志红，教授，博导，研究方向：图像处理、视频拼接和目标跟踪、嵌入式处理等，E-mail: xizhihong@hrbeu.edu.cn
  • 基金资助:

    国家自然科学基金(No.60875025)资助

     

Translational Motion Estimation Combined with Improved C-1BT Transform in Global Motion

XI Zhi-hong, CHU Shou-yan   

  1. College of Information and Communication Engineering, Harbin Engineering University, Harbin 150001, China
  • Received:2011-10-31 Revised:2012-05-21 Online:2013-11-29 Published:2012-05-21

PDF

915

被引次数

2

摘要： 针对全局运动估计难以同时取得有效性和实时性的难题，结合限制型1比特变换法(constrained one-bit transform, C-1BT) 和自适应十字搜索法提出一种快速准确估计全局平移运动的方法. 该方法改进传统的C-1BT方法，利用自适应门限构造模板图像，并在提取图像的低比特面和模板图像时均提前判定零运动矢量区域. 根据图像间相似性测度可代表运动趋势强度这一原则设置自适应门限，选择一定运动幅度范围内可靠的宏块. 仿真结果表明，改进的自适应十字搜索法更充分地利用了时间和空间的有效性，在保证精度的同时极大地减少了搜索点数量.

关键词: 全局运动估计, 自适应十字搜索法, 低比特平面, 自适应门限

Abstract:  Considering the difficulty in achieving effectiveness and real-time property simultaneously in the global motion estimation, and combining the bit-plane method and ARPS, an accurate approach for estimating the global translational motion is proposed. It improves the traditional C-1BT method by setting an adaptive threshold to construct the mask image. Early determination of zero motion vector areas is achieved, along with twice extraction of low bit planes. The similarity measures between images can represent motion intensity.As such, by selecting a sub-region of the similarity measures within a certain range, these regions can better represent the global motion trends as compared with other regions. Finally, the improved ARPS approach takes full advantage of space-time correlation. Simulation results show that the proposed approach can increases accuracy and reduces the amount of computation.  

Key words: global motion estimation, adaptive rood pattern search (ARPS), low-bit-plane, adaptive threshold

中图分类号: