基于轮廓几何稀疏表示的刚性目标模型及其分级检测算法

林煜东; 和红杰; 陈帆; 尹忠科

doi:10.16383/j.aas.2015.c130431

基于轮廓几何稀疏表示的刚性目标模型及其分级检测算法

doi: 10.16383/j.aas.2015.c130431

1.
四川信号与信息处理重点实验室, 西南交通大学成都 610031;
2.
北京遥感信息研究所北京 100192

基金项目:

国家自然科学基金(61461047)资助

详细信息

作者简介:
林煜东西南交通大学信息科学与技术学院博士研究生.2006年获得华南农业大学信息学院学士学位.2009年获得华南农业大学信息学院硕士学位.主要研究方向为图像表示与图像目标检测.E-mail:willianlam@126.com

通讯作者:
陈帆博士,西南交通大学信息科学与技术学院副教授.主要研究方向为多媒体数据安全,图像处理,计算机应用技术.本文通信作者.E-mail:fchen@home.swjtu.edu.cn

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- 文章访问数: 2225
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- 被引次数: 0
出版历程
- 收稿日期: 2013-05-22
- 修回日期: 2014-11-15
- 刊出日期: 2015-04-20

A Rigid Object Detection Model Based on Geometric Sparse Representation of Profile and Its Hierarchical Detection Algorithm

1.
Sichuan Key Laboratory of Signal and Information Processing, Southwest Jiaotong University, Chengdu 610031;
2.
Beijing Institute of Remote Sensing Information, Beijing 100192

Funds:

Supported by National Natural Science Foundation of China(61461047)

摘要

摘要: 刚性目标轮廓具有明显几何特性且不易受光照、纹理和颜色等因素影响.结合上述特性和图像稀疏表示原理,提出一种适用于刚性目标的分级检测算法.在基于部件模型(Part-based model, PBM)的框架下,采用匹配追踪算法将目标轮廓自适应地稀疏表示为几何部件的组合,根据部件与目标轮廓的匹配度,构建描述部件空间关系的有序链式结构.利用该链式结构的有序特性逐级缩小待检测范围,以匹配度为权值对各级部件显著图进行加权融合生成目标显著图. PASCAL图像库上的检测结果表明,该检测方法对具有显著轮廓特征的刚性目标有较好的检测结果,检测时耗较现有算法减少约60%~90%.
- 目标检测 /
- 刚性目标轮廓 /
- 几何稀疏表示 /
- 分级检测
Abstract: The profile of rigid objects has the geometrical characteristic and is insusceptible to illumination, texture or color. In this paper, a hierarchical detection algorithm for ridge objects based on geometric sparse representation of profile is presented. In the framework of part-based model(PBM), the object profile is automatically divided into geometrical parts by the sparse representation using the matching pursuit algorithm. To describe the spatial relationship of the geometrical parts, an ordered chain-like structure is constructed according to the order of the matching degree of the parts and the object profile. With the ordered chain-like structure, the detection range is gradually shrunk at each hierarchy. The final salient map of the object is the weighted summation of the parts' salient maps, and the weights are defined as the matching degrees. The simulation on the PASCAL datasets shows that the proposed method outperforms the existing models in rigid objects detection, and saves 60% to 90% detection time compared to the state-of-art methods.
- Object detection /
- profile of rigid object /
- geometric sparse representation /
- hierarchical detection

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参考文献(29)

[1]	Li Wan-Yi, Wang Peng, Qiao Hong. A survey of visual attention based methods for object tracking. Acta Automatica Sinica, 2014, 40(4):561-576(黎万义, 王鹏, 乔红. 引入视觉注意机制的目标跟踪方法综述. 自动化学报, 2014, 40(4):561-576)
[2]	Jiang Xiao-Lian, Li Cui-Hua, Li Xiong-Zong. Saliency based tracking method for abrupt motions via two-stage sampling. 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. 自动化学报, 2014, 40(6):1098-1107)
[3]	[3] Wang X, Lv Q, Wang B, Zhang L M. Airport detection in remote sensing images:a method based on saliency map. Cognitive Neurodynamics, 2013, 7(2):143-154
[4]	Han Min, Zheng Dan-Chen. Shape recognition based on fuzzy shape context. Acta Automatica Sinica, 2012, 38(1):68-75(韩敏, 郑丹晨. 基于模糊形状上下文特征的形状识别算法. 自动化学报, 2012, 38(1):68-75)
[5]	[5] Lin Y D, He H J, Yin Z K, Chen F. Rotation-invariant object detection in remote sensing images based on radial-gradient angle. IEEE Geoscience and Remote Sensing Letters, 2015, 12(4):746-750
[6]	[6] Sun H, Sun X, Wang H Q, Li Y, Li X J. Automatic target detection in high-resolution remote sensing images using spatial sparse coding bag-of-words model. IEEE Geoscience and Remote Sensing Letter, 2012, 9(1):109-113
[7]	[7] Liu L, Shi Z W. Airplane detection based on rotation invariant and sparse coding in remote sensing images. Optik, 2014, 125(18):5327-5333
[8]	[8] Lei Z, Fang T, Huo H, Li D R. Rotation-invariant object detection of remotely sensed images based on texton forest and Hough voting. IEEE Transactions on Geoscience and Remote Sensing, 2012, 50(4):1206-1217
[9]	[9] Xu J, Sun X, Zhang D B, Fu K. Automatic detection of inshore ships in high-resolution remote sensing images using robust invariant generalized Hough transform. IEEE Geoscience and Remote Sensing Letters, 2014, 11(12):2070-2074
[10]	Csurka G, Dance C R, Fan L X, Willamowski J, Bray C. Visual categorization with bags of keypoints. In:Proceedings of the 2004 European Conference on Computer Vision. Prague, Czech Republic:Springer, 2004. 1-22
[11]	Felzenszwalb P F, Huttenlocher D P. Pictorial structures for object recognition. International Journal of Computer Vision, 2005, 61(1):55-79
[12]	Song X, Luo P, Lin L, Jia Y D. A discriminative model for object representation and detection via sparse features. In:Proceedings of the 20th International Conference on Pattern Recognition. Istanbul, Turkey:IEEE, 2010. 3077-3080
[13]	Sun X, Wang H Q, Fu K. Automatic detection of geospatial objects using taxonomic semantics. IEEE Geoscience and Remote Sensing Letters, 2010, 7(1):23-27
[14]	Felzenszwalb P F, Girshick R B, McAllester D. Object detection with discriminatively trained part-based models. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2010, 32(9):1627-1645
[15]	Zhu L, Chen Y H, Yuille A, Freeman W. Latent hierarchical structural learning for object detection. In:Proceedings of the 2010 IEEE Conference on Computer Vision and Pattern Recognition. San Francisco, CA, USA:IEEE, 2010. 1062-1069
[16]	Lu C E, Adluru N, Ling H B, Zhu G X, Latecki L J. Contour based object detection using part bundles. Computer Vision and Image Understanding, 2010, 114(7):827-834
[17]	Zhang H G, Wang J X, Bai X, Zhou J, Cheng J, Zhao H J. Object detection via foreground contour feature selection and part-based shape model. In:Proceedings of the 21st International Conference on Pattern Recognition. Tsukuba, Japan:IEEE, 2012. 2524-2527
[18]	Xi Hui-Ting. Study on Rigid Moving Target Tracking Algorithm [Master dissertation], East China Normal University, China, 2008.(奚慧婷. 刚性运动目标的跟踪算法研究 [硕士学位论文], 华东师范大学, 中国, 2008.)
[19]	Fergus R, Perona P, Zisserman A. Object class recognition by unsupervised scale-invariant learning. In:Proceedings of the 2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. Madison, Wisconsin, USA:IEEE, 2003. Ⅱ-264-Ⅱ-271
[20]	Crandall D, Felzenszwalb P F, Huttenlocher D P. Spatial priors for part-based recognition using statistical models. In:Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. San Diego, CA, USA:IEEE, 2005. 10-17
[21]	Felzenszwalb P F, Huttenlocher D P. Efficient matching of pictorial structures. In:Proceedings of the 2000 IEEE Conference on Computer Vision and Pattern Recognition. Hilton Head Island, South Carolina, USA:IEEE, 2000. 66-73
[22]	Carneiro G, Lowe D. Sparse flexible models of local features. In:Proceedings of the 9th European Conference on Computer Vision. Graz, Austria:Springer, 2006. 29-43
[23]	Wang Jian-Ying, Yin Zhong-Ke, Zhang Chun-Mei. The Sparse Decomposition and Application for Signals and Images. Chengdu:Southwest Jiaotong University Press, 2006. 49-57(王建英, 尹忠科, 张春梅. 信号与图像的稀疏分解及初步应用. 成都:西南交通大学出版社, 2006. 49-57)
[24]	Li Heng-Jian, Yin Zhong-Ke, Wang Jian-Ying. Image sparse decomposition based on quantum genetic algorithm. Journal of Southwest Jiaotong University, 2007, 42(1):19-23(李恒建, 尹忠科, 王建英. 基于量子遗传优化算法的图像稀疏分解. 西南交通大学学报, 2007, 42(1):19-23)
[25]	Yang Yang, Li Shan-Ping. Fast object detection with deformable part models and segment locations' hint. Acta Automatica Sinica, 2012, 38(4):540-548(杨扬, 李善平. 分割位置提示的可变形部件模型快速目标检测. 自动化学报, 2012, 38(4):540-548)
[26]	Dalal N, Triggs B. Histograms of oriented gradients for human detection. In:Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. San Diego, CA, USA:IEEE, 2005. 886-893
[27]	Neubeck A, van Gool L. Efficient non-maximum suppression. In:Proceedings of the 18th International Conference on Pattern Recognition. Hong Kong, China:IEEE, 2006. 850-855
[28]	Barinova O, Lempitsky V, Kohli P. On detection of multiple object instances using Hough transforms. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2012, 34(9):1773-1784
[29]	Lu Wen-Hao, Li Ya-Li, Wang Sheng-Jin, Ding Xiao-Qing. Improvements of 3D object detection with part-based models. Acta Automatica Sinica, 2012, 38(4):497-506(鹿文浩, 李亚利, 王生进, 丁晓青. 基于部件的三维目标检测算法新进展. 自动化学报, 2012, 38(4):497-506)

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基于轮廓几何稀疏表示的刚性目标模型及其分级检测算法

doi: 10.16383/j.aas.2015.c130431

作者简介:
林煜东西南交通大学信息科学与技术学院博士研究生.2006年获得华南农业大学信息学院学士学位.2009年获得华南农业大学信息学院硕士学位.主要研究方向为图像表示与图像目标检测.E-mail:willianlam@126.com

通讯作者:
陈帆博士,西南交通大学信息科学与技术学院副教授.主要研究方向为多媒体数据安全,图像处理,计算机应用技术.本文通信作者.E-mail:fchen@home.swjtu.edu.cn

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A Rigid Object Detection Model Based on Geometric Sparse Representation of Profile and Its Hierarchical Detection Algorithm

计量

目录

留言板

基于轮廓几何稀疏表示的刚性目标模型及其分级检测算法

doi: 10.16383/j.aas.2015.c130431

作者简介: 林煜东 西南交通大学信息科学与技术学院博士研究生.2006年获得华南农业大学信息学院学士学位.2009年获得华南农业大学信息学院硕士学位.主要研究方向为图像表示与图像目标检测.E-mail:willianlam@126.com

通讯作者: 陈帆 博士,西南交通大学信息科学与技术学院副教授.主要研究方向为多媒体数据安全,图像处理,计算机应用技术.本文通信作者.E-mail:fchen@home.swjtu.edu.cn

计量

出版历程

A Rigid Object Detection Model Based on Geometric Sparse Representation of Profile and Its Hierarchical Detection Algorithm

计量

出版历程

目录

作者简介:
林煜东西南交通大学信息科学与技术学院博士研究生.2006年获得华南农业大学信息学院学士学位.2009年获得华南农业大学信息学院硕士学位.主要研究方向为图像表示与图像目标检测.E-mail:willianlam@126.com

通讯作者:
陈帆博士,西南交通大学信息科学与技术学院副教授.主要研究方向为多媒体数据安全,图像处理,计算机应用技术.本文通信作者.E-mail:fchen@home.swjtu.edu.cn