Novel virtual boundary detection method based on deep learning

doi:10.11772/j.issn.1001-9081.2018041347

Journal of Computer Applications ›› 2018, Vol. 38 ›› Issue (11): 3211-3215.DOI: 10.11772/j.issn.1001-9081.2018041347

Previous Articles Next Articles

Novel virtual boundary detection method based on deep learning

LAI Chuanbin¹, HAN Yuexing^1,2, GU Hui^3,4, WANG Bing¹

1. School of Computer Engineering and Science, Shanghai University, Shanghai 200444, China;
2. Shanghai Institute for Advanced Communication and Data Science, Shanghai University, Shanghai 200444, China;
3. Material Genome Institute, Shanghai University, Shanghai 200444, China;
4. School of Materials Science and Engineering, Shanghai University, Shanghai 200444, China

Received:2018-04-30 Revised:2018-06-25 Online:2018-11-10 Published:2018-11-10
Supported by:
This work is partially supported by the National Science Foundation for Young Scientists of China (61603237), the Shanghai Pujiang Program (17PJ1402900), the Program for Professor of Special Appointment (Eastern Scholar) at at Shanghai Institutions of Higher Learning.

基于深度学习的虚拟边界检测方法

赖传滨¹, 韩越兴^1,2, 顾辉^3,4, 王冰¹

1. 上海大学计算机工程与科学学院, 上海 200444;
2. 上海大学上海先进通信与数据科学研究院, 上海 200444;
3. 上海大学材料基因组工程研究院, 上海 200444;
4. 上海大学材料科学与工程学院, 上海 200444

通讯作者: 韩越兴
作者简介:赖传滨(1994-),男,江西赣州人,硕士研究生,主要研究方向:材料图像处理、计算机视觉、机器学习;韩越兴(1975-),男,黑龙江海伦人,讲师,博士,主要研究方向:材料图像处理、分子机器人、计算机视觉、机器视觉;顾辉(1963-),男,江苏如皋人,教授,博士,主要研究方向:先进材料分层的相-微观结构-界面关系、微观结构的定量分析方法;王冰(1978-),女,山东掖县人,教授,博士,主要研究方向:复杂网络、鲁棒网络结构、网络同步性设计。
基金资助:
国家青年科学基金资助项目（61603237）；上海市浦江人才计划项目（17PJ1402900）；上海高校特聘教授（东方学者）岗位计划。

Abstract

Abstract: Traditional edge detection methods can not accurately detect the Virtual Boundary (VB) between different regions in materials microscopic images. In order to solve this problem, a virtual boundary detection model based on Convolutional Neural Network (CNN) called Virtual Boundary Net (VBN) was proposed. The VGGNet (Visual Geometry Group Net) deep learning model was simplified, and dropout and Adam algorithms were applied in the training process. An image patch centered on each pixel in the image was extracted as the input, and the class of the image patch was output to decide whether the center pixel belongs to the virtual boundary or not. In the experiment of virtual boundary detection for two kinds of material images, the average detection precision of this method reached 92.5%, and the average recall rate reached 89.5%. The experimental results prove that the VBN can detect the virtual boundary in the image accurately and effectively, which is an alternative method to low-efficient manual analysis.

Key words: virtual boundary detection, edge detection, Convolutional Neural Network (CNN), deep learning, image segmentation

摘要： 针对传统边缘检测方法无法对材料微观图像中不同区域间存在的"虚拟边界"（VB）进行准确检测的问题，提出一种基于卷积神经网络（CNN）的虚拟边界检测模型，称之为"虚拟边界网络"（VBN）。该模型对VGGNet深度学习模型进行了简化，并在模型训练过程中采用了dropout以及Adam算法等优化策略。VBN以图像中每个像素为中心所取的图像块作为输入，然后输出该图像块所属的类别并据此判断中心像素是否属于虚拟边界。在对两类材料图像进行虚拟边界检测的实验中，VBN的平均检测精度到达92.5%，平均召回率达到89.5%，证明该模型能够准确、有效地对图像中的虚拟边界进行检测，是一种替代低效率人工分析方法的有效手段。

关键词: 虚拟边界检测, 边缘检测, 卷积神经网络, 深度学习, 图像分割

CLC Number:

TP399

LAI Chuanbin, HAN Yuexing, GU Hui, WANG Bing. Novel virtual boundary detection method based on deep learning[J]. Journal of Computer Applications, 2018, 38(11): 3211-3215.

赖传滨, 韩越兴, 顾辉, 王冰. 基于深度学习的虚拟边界检测方法[J]. 计算机应用, 2018, 38(11): 3211-3215.

References

[1] KORNIY V, RUSYN B, LYSAK Y, et al. Quantitative analysis of metallographic images[C]//Proceedings of the 2006 International Conference on Modern Problems of Radio Engineering, Telecommunications, and Computer Science. Piscataway, NJ:IEEE, 2006:251-252.
[2] 邓仕超, 刘铁根, 萧泽新. 应用Canny算法和灰度等高线的金相组织封闭边缘提取[J]. 光学精密工程, 2010, 18(10):2314-2323.(DENG S C, LIU T G, XIAO Z X. Extraction of enclosing image edge for metallographic structure based on Canny and grayscale contour line[J]. Optics and Precision Engineering, 2010, 18(10):2314-2323.)
[3] KRIZHEVSKY A, SUTSKEVER I, HINTON G E. ImageNet classification with deep convolutional neural networks[C]//Proceedings of the 2012 International Conference on Neural Information Processing Systems. Lake Tahoe, Nevada:Curran Associates Inc., 2012:1097-1105.
[4] LECUN Y, BOTTOU L, BENGIO Y, et al. Gradient-based learning applied to document recognition[J]. Proceedings of the IEEE, 1998, 86(11):2278-2324.
[5] SIMONYAN K, ZISSERMAN A. Very deep convolutional networks for large-scale image recognition[EB/OL].[2018-03-20].http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.740.6937&rep=rep1&type=pdf.
[6] MUTHUKRISHNAN. R, RADHA M. Edge detection techniques for image segmentation[J]. International Journal of Computer Science & Information Technology, 2012, 3(6):250-254.
[7] CANNY J. A computational approach to edge detection[J]. IEEE Transactions on Pattern Analysis & Machine Intelligence, 1987, 8(6):184-203.
[8] MARTIN D R, FOWLKES C C, MALIK J. Learning to detect natural image boundaries using local brightness, color, and texture cues[J]. IEEE Transactions on Pattern Analysis & Machine Intelligence, 2004, 26(5):530-549.
[9] DOLLAR P, ZITNICK C L. Structured forests for fast edge detection[C]//Proceedings of the 2013 IEEE International Conference on Computer Vision. Washington, DC:IEEE Computer Society, 2014:1841-1848.
[10] LIM J J, ZITNICK C L, DOLLAR P. Sketch tokens:a learned mid-level representation for contour and object detection[C]//Proceedings of the 2013 IEEE Conference on Computer Vision and Pattern Recognition. Washington, DC:IEEE Computer Society, 2013:3158-3165.
[11] GANIN Y, LEMPITSKY V. N⁴-Fields:neural network nearest neighbor fields for image transforms[C]//ACCV 2014:Asian Conference on Computer Vision. Berlin:Springer, 2014:536-551.
[12] BERTASIUS G, SHI J, TORRESANI L. DeepEdge:a multi-scale bifurcated deep network for top-down contour detection[C]//Proceedings of the 2015 IEEE Conference on Computer Vision and Pattern Recognition. Washington, DC:IEEE Computer Society, 2015:4380-4389.
[13] SHEN W, WANG X, WANG Y, et al. DeepContour:a deep convolutional feature learned by positive-sharing loss for contour detection[C]//Proceedings of the 2015 IEEE Conference on Computer Vision and Pattern Recognition. Washington, DC:IEEE Computer Society, 2015:3982-3991.
[14] ANANYEV M, GAVRILYUK A, MITRI S, et al. Cu and Gd co-doped BaCeO3, proton conductors:Experimental vs SEM image algorithmic-segmentation results[J]. Electrochimica Acta, 2014, 125(12):371-379.
[15] LOPEZ P, LIRA J, HEIN I. Discrimination of ceramic types using digital image processing by means of morphological filters[J]. Archaeometry, 2014, 57(1):146-162.
[16] HAHA M B, GALLUCCI E, GUIDOUM A, et al. Relation of expansion due to alkali silica reaction to the degree of reaction measured by SEM image analysis[J]. Cement & Concrete Research, 2007, 37(8):1206-1214.
[17] 赵曌, 丁广太, 樊明磊,等. 融合LBP纹理和局部灰度特征的材料图像分割[J]. 计算机技术与发展, 2016, 26(10):11-16.(ZHAO Z, DING G T, FAN M L, et al. Material image segmentation combined LBP texture and local gray level feature[J]. Computer Technology and Development, 2016, 26(10):11-16.)
[18] CHEN Y, CHEN J. A watershed segmentation algorithm based on ridge detection and rapid region merging[C]//Proceedings of the 2014 IEEE International Conference on Signal Processing, Communications and Computing. Piscataway, NJ:IEEE, 2014:420-424.
[19] AZIMI S M, BRITZ D, ENGSTLER M, et al. Advanced steel microstructural classification by deep learning methods[EB/OL].[2018-03-20].http://pubman.mpdl.mpg.de/pubman/item/escidoc:2460757:2/component/escidoc:2460756/arXiv:1706.06480.pdf.
[20] SRIVASTAVA N, HINTON G, KRIZHEVSKY A, et al. Dropout:a simple way to prevent neural networks from overfitting[J]. Journal of Machine Learning Research, 2014, 15(1):1929-1958.
[21] KINGMA D P, BA J. Adam:a method for stochastic optimization[EB/OL].[2018-03-20].http://yeolab.weebly.com/uploads/2/5/5/0/25509700/a_method_for_stochastic_optimization_.pdf.

Novel virtual boundary detection method based on deep learning

基于深度学习的虚拟边界检测方法

PDF

Knowledge

Abstract

Cite this article

share this article

References

Related Articles 15

Recommended Articles

Metrics

[1]	Yexin PAN, Zhe YANG. Optimization model for small object detection based on multi-level feature bidirectional fusion [J]. Journal of Computer Applications, 2024, 44(9): 2871-2877.
[2]	Yun LI, Fuyou WANG, Peiguang JING, Su WANG, Ao XIAO. Uncertainty-based frame associated short video event detection method [J]. Journal of Computer Applications, 2024, 44(9): 2903-2910.
[3]	Yunchuan HUANG, Yongquan JIANG, Juntao HUANG, Yan YANG. Molecular toxicity prediction based on meta graph isomorphism network [J]. Journal of Computer Applications, 2024, 44(9): 2964-2969.
[4]	Shunyong LI, Shiyi LI, Rui XU, Xingwang ZHAO. Incomplete multi-view clustering algorithm based on self-attention fusion [J]. Journal of Computer Applications, 2024, 44(9): 2696-2703.
[5]	Jing QIN, Zhiguang QIN, Fali LI, Yueheng PENG. Diagnosis of major depressive disorder based on probabilistic sparse self-attention neural network [J]. Journal of Computer Applications, 2024, 44(9): 2970-2974.
[6]	Xiyuan WANG, Zhancheng ZHANG, Shaokang XU, Baocheng ZHANG, Xiaoqing LUO, Fuyuan HU. Unsupervised cross-domain transfer network for 3D/2D registration in surgical navigation [J]. Journal of Computer Applications, 2024, 44(9): 2911-2918.
[7]	Hong CHEN, Bing QI, Haibo JIN, Cong WU, Li’ang ZHANG. Class-imbalanced traffic abnormal detection based on 1D-CNN and BiGRU [J]. Journal of Computer Applications, 2024, 44(8): 2493-2499.
[8]	Yuhan LIU, Genlin JI, Hongping ZHANG. Video pedestrian anomaly detection method based on skeleton graph and mixed attention [J]. Journal of Computer Applications, 2024, 44(8): 2551-2557.
[9]	Yanjie GU, Yingjun ZHANG, Xiaoqian LIU, Wei ZHOU, Wei SUN. Traffic flow forecasting via spatial-temporal multi-graph fusion [J]. Journal of Computer Applications, 2024, 44(8): 2618-2625.
[10]	Qianhong SHI, Yan YANG, Yongquan JIANG, Xiaocao OUYANG, Wubo FAN, Qiang CHEN, Tao JIANG, Yuan LI. Multi-granularity abrupt change fitting network for air quality prediction [J]. Journal of Computer Applications, 2024, 44(8): 2643-2650.
[11]	Zhe KONG, Han LI, Shaowei GAN, Mingru KONG, Bingtao HE, Ziyu GUO, Ducheng JIN, Zhaowen QIU. Structure segmentation model for 3D kidney images based on asymmetric multi-decoder and attention module [J]. Journal of Computer Applications, 2024, 44(7): 2216-2224.
[12]	Yiqun ZHAO, Zhiyu ZHANG, Xue DONG. Anisotropic travel time computation method based on dense residual connection physical information neural networks [J]. Journal of Computer Applications, 2024, 44(7): 2310-2318.
[13]	Yangyi GAO, Tao LEI, Xiaogang DU, Suiyong LI, Yingbo WANG, Chongdan MIN. Crowd counting and locating method based on pixel distance map and four-dimensional dynamic convolutional network [J]. Journal of Computer Applications, 2024, 44(7): 2233-2242.
[14]	Song XU, Wenbo ZHANG, Yifan WANG. Lightweight video salient object detection network based on spatiotemporal information [J]. Journal of Computer Applications, 2024, 44(7): 2192-2199.
[15]	Xun SUN, Ruifeng FENG, Yanru CHEN. Monocular 3D object detection method integrating depth and instance segmentation [J]. Journal of Computer Applications, 2024, 44(7): 2208-2215.