首页 > 论文 > 光学学报 > 38卷 > 3期(pp:315002--1)

基于双目视觉的显著性目标检测方法

Salient Object Detection Method Based on Binocular Vision

  • 摘要
  • 论文信息
  • 参考文献
  • 被引情况
  • PDF全文
分享:

摘要

针对现有的显著性目标检测算法在受到相似背景干扰时, 易出现目标检测准确度低、稳定性差的问题, 提出一种基于双目视觉的显著性目标检测方法。受人眼视觉特性启发, 将双目视觉模型感知的深度信息作为显著性特征与多特征聚类分割结果进行协同处理, 定量分析图像区域级的深度显著性, 再将全局显著性与区域深度显著性进行加权融合, 突出目标区域, 根据融合结果的区域分布进行背景抑制, 完成显著性目标的检测。实验结果表明, 与现有的显著性目标检测算法相比, 该算法有效地抑制了相似背景的干扰, 并且准确度高、稳定性好。

Abstract

Aiming at the problem that the existing salient object detection algorithms suffers from the similar background interference, the detection accuracy of the target is low and the stability is poor. We propose a salient object detection method based on binocular vision. Firstly, inspired by the visual characteristics of the human eye, we consider the depth information acquired by binocular vision model as the salient features based on human visual characteristics. Secondly, we use the depth information and the result of region segmentation based on multi-feature fusion clustering to analyze the regional level depth saliency of image quantitatively. Thirdly, we make the weighted fusion of the global saliency map and regional level depth saliency map to highlight the objection area. Finally, we suppress the background to complete salient object detection based on the regional distribution of fusion results. The results show that compared with the existing methods, the proposed method can effectively suppress the interference of similar background with high accuracy and stability simultaneously.

Newport宣传-MKS新实验室计划
补充资料

中图分类号:TP242.6

DOI:10.3788/aos201838.0315002

所属栏目:机器视觉

基金项目:江苏省重点研发计划(BE2016071, BE2017648)

收稿日期:2017-08-09

修改稿日期:2017-10-08

网络出版日期:--

作者单位    点击查看

李庆武:河海大学物联网工程学院, 江苏 常州 213022常州市传感网与环境感知重点实验室, 江苏 常州 213022
周亚琴:河海大学物联网工程学院, 江苏 常州 213022
马云鹏:河海大学物联网工程学院, 江苏 常州 213022
邢俊:河海大学物联网工程学院, 江苏 常州 213022
许金鑫:河海大学物联网工程学院, 江苏 常州 213022

联系人作者:李庆武(li_qingwu@163.com)

备注:李庆武(1964-), 男, 博士, 教授, 主要从事智能感知与图像处理方面的研究。E-mail: li_qingwu@163.com

【1】Ren Z X, Gao S H, Chia L T, et al. Region-based saliency detection and its application in object recognition[J]. IEEE Transactions on Circuits and Systems for Video Technology, 2014, 24(5): 769-779.

【2】Yang L N, An W, Lin Z P, et al. Small target detection based on visual saliency improved by spatial distance[J]. Acta Optica Sinica, 2015, 35(7): 0715004.
杨林娜, 安玮, 林再平, 等. 基于空间距离改进的视觉显著性弱小目标检测[J]. 光学学报, 2015, 35(7): 0715004.

【3】Wang B, Su Y M, Wan L, et al. Sea sky line detection method of unmanned surface vehicle based on gradient saliency[J]. Acta Optica Sinica, 2016, 36(5): 0511002.
王博, 苏玉民, 万磊, 等. 基于梯度显著性的水面无人艇的海天线检测方法[J]. 光学学报, 2016, 36(5): 0511002.

【4】Zhu J Y, Wu J J, Xu Y, et al. Unsupervised object class discovery via saliency-guided multiple class learning[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2015, 37(4): 862-875.

【5】Jiang X L, Li C H, Li X Z. Saliencybased tracking method for abrupt motions via two-stage sampling[J]. Acta Automatica Sinica, 2014, 40(6): 1098-1107.
江晓莲, 李翠华, 李雄宗. 基于视觉显著性的两阶段采样突变目标跟踪算法[J]. 自动化学报, 2014, 40(6): 1098-1107.

【6】Yang X Y, Qian X M, Xue Y. Scalable mobile image retrieval by exploring contextual saliency[J]. IEEE Transactions on Image Processing, 2015, 24(6): 1709-1721.

【7】Borji A, Cheng M M, Jiang H Z, et al. Salient object detection: a benchmark[J]. IEEE Transactions on Image Processing, 2015, 24(12): 5706-5722.

【8】Ma Y F, Zhang H J. Contrast-based image attention analysis by using fuzzy growing[C]∥Proceedings of the eleventh ACM international conference on Multimedia, 2003: 374-381.

【9】Achanta R, Estrada F, Wils P, et al. Salient region detection and segmentation[C]∥Proceedings of the 6th international conference on Computer vision systems, 2008: 66-75.

【10】Zhai Y, Shah M. Visual attention detection in video sequences using spatiotemporal cues[C]∥Proceedings of the 14th ACM international conference on Multimedia, 2006: 815-824.

【11】Achanta R, Hemami S, Estrada F, et al. Frequency-tuned salient region detection[J]. IEEE International Conference on Computer Vision and Pattern Recognition, 2009, 22(9/10): 1597-1604.

【12】Cheng M M, Mitra N J, Huang X L, et al. Global contrast based salient region detection[C]. IEEE Conference on Computer Vision and Pattern Recognition, 2011, 37(3): 569-582.

【13】Song T F, Liu Z Y. Saliency detection based on center rectangle composition prior[J]. Journal of Image and Graphics, 2017, 22(3): 315-326.
宋腾飞, 刘政怡. 中心矩形构图先验的显著目标检测[J]. 中国图象图形学报, 2017, 22(3): 315-326.

【14】Li X H, Lu H C, Zhang L H, et al. Saliency detection via dense and sparse reconstruction[C]. IEEE International Conference on Computer Vision, 2013: 2976-2983.

【15】Zhu W J, Liang S, Wei Y C, et al. Saliency optimization from robust background detection[C]. IEEE Conference on Computer Vision and Pattern Recognition, 2014: 1049/1050(8): 2814-2821.

【16】Zhou S J, Ren F J, Du J, et al. Salient region detection based on the integration of background-bias prior and center-bias prior[J]. Journal of Image and Graphics, 2017, 22(5): 584-595.
周帅骏, 任福继, 堵俊, 等. 融合背景先验与中心先验的显著性目标检测[J]. 中国图象图形学报, 2017, 22(5): 584-595.

【17】Lin H F, Li J, Liu G D, et al. Saliency detection method using adaptive background template and spatial prior[J]. Acta Automatica Sinica, 2017, 43(10): 1736-1748.
林华锋, 李静, 刘国栋, 等. 基于自适应背景模板与空间先验的显著性物体检测方法[J]. 自动化学报, 2017, 43(10): 1736-1748.

【18】Chen H Y, Qie L Z, Yang D D, et al. Visual background extraction algorithm based on superpixel information feedback[J]. Acta Optica Sinica, 2017, 37(7): 0715001.
陈海永, 郄丽忠, 杨德东, 等. 基于超像素信息反馈的视觉背景提取算法[J]. 光学学报, 2017, 37(7): 0715001.

【19】Achanta R, Shaji A, Smith K, et al. SLIC superpixels compared to state-of-the-art superpixel methods[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2012, 34(11): 2274-2282.

【20】Song X Y, Zhou L L, Li Z G, et al. Review on superpixel methods in image segmentation[J]. Journal of Image and Graphics, 2015, 20(5): 599-608.
宋熙煜, 周利莉, 李中国, 等. 图像分割中的超像素方法研究综述[J]. 中国图象图形学报, 2015, 20(5): 599-608.

【21】Goferman S, Zelnik-Manor L, Tal A. Context-aware saliency detection[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2012, 34(10): 1915-1926.

【22】Rahtu E, Kannala J, Salo M, et al. Segmenting salient objects from images and videos[J]. European Conference on Computer Vision, 2010, 6315: 366-379.

【23】Margolin R, Tal A, Zelnik-Manor L. What makes a patch distinct?[J]. IEEE Conference on Computer Vision and Pattern Recognition, 2013, 9(4): 1139-1146.

【24】Yang C, Zhang L H, Lu H C. Graph-regularized saliency detection with convex-hull-based center prior[J]. IEEE Signal Processing Letters, 2013, 20(7): 637-640.

【25】Li H Y, Lu H C, Lin Z, et al. Inner and inter label propagation: salient object detection in the wild[J]. IEEE Transactions on Image Processing, 2015, 24(10): 3176-3186.

【26】Huang F, Qi J Q, Lu H C, et al. Salient object detection via multiple instance learning[J]. IEEE Transactions on Image Processing, 2017, 26(4): 1911-1922.

引用该论文

Li Qingwu,Zhou Yaqin,Ma Yunpeng,Xing Jun,Xu Jinxin. Salient Object Detection Method Based on Binocular Vision[J]. Acta Optica Sinica, 2018, 38(3): 0315002

李庆武,周亚琴,马云鹏,邢俊,许金鑫. 基于双目视觉的显著性目标检测方法[J]. 光学学报, 2018, 38(3): 0315002

被引情况

【1】冯晨霄,汪西莉. 融合特征和决策的卷积-反卷积图像分割模型. 激光与光电子学进展, 2019, 56(1): 11008--1

【2】金一康,于凤芹. 基于背景连续性先验知识的显著性检测. 激光与光电子学进展, 2019, 56(12): 121006--1

您的浏览器不支持PDF插件,请使用最新的(Chrome/Fire Fox等)浏览器.或者您还可以点击此处下载该论文PDF