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基于单应性扩散约束的二步网格优化视差图像对齐

陈殷齐 郑慧诚 严志伟 林峻宇

陈殷齐, 郑慧诚, 严志伟, 林峻宇. 基于单应性扩散约束的二步网格优化视差图像对齐. 自动化学报, 2022, 48(x): 1−14 doi: 10.16383/j.aas.c210966
引用本文: 陈殷齐, 郑慧诚, 严志伟, 林峻宇. 基于单应性扩散约束的二步网格优化视差图像对齐. 自动化学报, 2022, 48(x): 1−14 doi: 10.16383/j.aas.c210966
Chen Yin-Qi, Zheng Hui-Cheng, Yan Zhi-Wei, Lin Jun-Yu. Parallax image alignment with two-stage mesh optimization based on homography diffusion constraints. Acta Automatica Sinica, 2022, 48(x): 1−14 doi: 10.16383/j.aas.c210966
Citation: Chen Yin-Qi, Zheng Hui-Cheng, Yan Zhi-Wei, Lin Jun-Yu. Parallax image alignment with two-stage mesh optimization based on homography diffusion constraints. Acta Automatica Sinica, 2022, 48(x): 1−14 doi: 10.16383/j.aas.c210966

基于单应性扩散约束的二步网格优化视差图像对齐

doi: 10.16383/j.aas.c210966
基金项目: 国家自然科学基金 (61976231), 广东省基础与应用基础研究基金 (2019A1515011869), 广州市科技计划项目 (201803030029) 资助
详细信息
    作者简介:

    陈殷齐:中山大学计算机学院硕士研究生. 主要研究方向为图像对齐与拼接. E-mail: chenyq277@mail2.sysu.edu.cn

    郑慧诚:中山大学计算机学院副教授. 2004年获得法国里尔一大博士学位. 主要研究方向为计算机视觉, 神经网络和机器学习. 本文通信作者. E-mail: zhenghch@mail.sysu.edu.cn

    严志伟:中山大学计算机学院硕士研究生. 主要研究方向为深度学习, 目标检测. E-mail: yanzhw5@mail2.sysu.edu.cn

    林峻宇:中山大学电子与信息工程学院本科生. 主要研究方向为自动驾驶. E-mail: linjy95@mail2.sysu.edu.cn

Parallax Image Alignment with Two-stage Mesh Optimization Based on Homography Diffusion Constraints

Funds: Supported by National Natural Science Foundation of China (61976231), Guangdong Basic and Applied Basic Research Foundation (2019A1515011869), and Science and Technology Program of Guangzhou (201803030029)
More Information
    Author Bio:

    Chen Yin-Qi Master student at the School of Computer Science and Engineering, Sun Yat-sen University. His main research interest is image alignment and stitching

    Zheng Hui-Cheng Associate professor at the School of Computer Science and Engineering, Sun Yat-sen University. He received his Ph.D. degree from University of Lille 1, France, in 2004. His research interest covers computer vision, neural networks, and machine learning. Corresponding author of this paper

    Yan Zhi-Wei Master student at the School of Computer Science and Engineering, Sun Yat-sen University. His research interest covers deep learning, object detection

    Lin Jun-Yu Undergraduate student at the School of Electronics and Information Technology, Sun Yat-sen University. His main research interest is autonomous vehicles

  • 摘要: 目前, 在带有视差场景的图像对齐中, 主要难点在某些无法找到足够匹配特征的区域, 这些区域称为匹配特征缺失区域. 现有算法往往忽略匹配特征缺失区域的对齐建模, 而只将有足够匹配特征区域中的部分单应变换系数(如相似性变换系数)传递给匹配特征缺失区域, 或者采用将匹配特征缺失区域转化为有足够匹配特征区域的间接方式, 因此对齐效果仍不理想. 在客观事实上, 位于相同平面的区域应该拥有相同的完整单应变换而非部分变换参数. 由此出发, 利用单应变换系数扩散的思想设计了一个二步网格优化的图像对齐算法. 该方法在第一步网格优化时获得有足够匹配特征区域的单应变换, 再基于提出的单应性扩散约束将这些单应变换系数扩散到邻域网格, 进行第二步网格优化, 在保证优化任务简洁高效的前提下实现单应变换系数的传播与图像对齐. 相较于现有的针对视差场景图像对齐算法, 所提方法在各项指标上都获得了更好的效果.
  • 图  1  HDW优势实例

    Fig.  1  Examples of HDW advantages

    图  2  HDW对齐示意图.

    Fig.  2  The alignment process of HDW.

    图  3  平面分割特性的分析

    Fig.  3  Analysis of plane segmentation characteristics

    图  4  各图像对上量化指标的直观对比

    Fig.  4  Intuitive comparison of the quantitative indicators on all the image pairs

    图  5  Plant实例上的对齐结果对比

    Fig.  5  Comparison of the alignment results on the Plant case

    图  6  Carpark实例上的对齐结果对比

    Fig.  6  Comparison of the alignment results on the Carpark case

    图  7  Stationery实例上的对齐结果对比

    Fig.  7  Comparison of the alignment results on the Stationery case

    图  8  Temple与Railtrack实例上的对齐结果对比

    Fig.  8  Comparison of the alignment results on the Temple and Railtrack cases

    图  9  HDW仍然有提升空间的实例

    Fig.  9  Examples where HDW still has room for improvement

    图  10  HDW的更多对齐结果实例

    Fig.  10  Alignment results of HDW on more examples

    表  1  HDW相对其他算法在Err、PSNR和SSIM上的平均改进(%)

    Table  1  The average improvement of HDW compared with other algorithms on Err, PSNR, and SSIM(%)

    SMH[17]PCPS[18]APAP[6]CPW[8]LLPC[37]ACW[41]
    Err−63.80−66.07−67.15−57.50−71.78−54.49
    PSNR+4.59+9.01+8.02+7.48+13.46+7.12
    SSIM+6.24+8.48+8.65+10.33+36.45+5.67
    下载: 导出CSV

    表  2  图像对的对齐效果量化指标对比

    Table  2  Quantitative comparison of alignment performance on image pairs

    PlantCarparkStationery
    SMH[17]Err1.23381.26591.0580
    PSNR15.749612.375822.9365
    SSIM0.65160.571860.9316
    PCPS[18]Err0.93961.19230.6695
    PSNR15.331411.555023.5924
    SSIM0.68580.56870.9355
    APAP[6]Err4.18541.13371.0154
    PSNR13.299511.936123.4257
    SSIM0.62450.63540.9236
    CPW[8]Err6.37181.64350.9038
    PSNR12.739711.203423.4680
    SSIM0.48180.58620.9258
    ACW[41]Err3.43020.79180.8070
    PSNR13.215912.073823.6319
    SSIM0.65890.65050.9278
    HDWErr0.27410.27870.5134
    PSNR18.196813.501924.2972
    SSIM0.82210.71430.9400
    下载: 导出CSV

    表  3  其他算法相对HDW在耗时上的对比

    Table  3  Temporal costs of other algorithms compared with that of HDW

    方法耗时占比(%)实际耗时(ms)
    HDW100106ms
    SMH[17]12561331
    PCPS[18]5423575
    ARAP[6]19351220511
    CPW[8]4548
    LLPC[37]298316
    ACW[41]20887422140
    下载: 导出CSV
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  • 收稿日期:  2021-10-13
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