Journal of Shandong University(Engineering Science) ›› 2020, Vol. 50 ›› Issue (4): 22-27.doi: 10.6040/j.issn.1672-3961.0.2019.416

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Depth segment classification algorithm based on convolutional neural network

ZHAO Ningning, TANG Xuesong*, ZHAO Mingbo   

  1. College of Information Science and Technology, Donghua University, Shanghai 201620, China
  • Published:2020-08-13

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Abstract: In order to solve the problem that redundant pixels in monocular images influenced depth information detection, a depth segment classification algorithm based on the convolutional neural network was proposed. We used NYU-Depth dataset to detect the segment-based features. Afterward, depth information was represented by line segments and its labels by the data preprocessing. The convolutional neural network was designed for considering the characteristics of the segments, and the classification of depth segments in monocular images was realized. By conducting several multi-group comparison experiments on different hyper-parameters, the accuracy of depth segment classification reached 73.50%. This experimental results proved the implement ability of the depth segment classification based on convolutional neural network, which was helpful to deep estimation using geometric features of images.

Key words: monocular image, depth estimation, convolutional network, depth segment, classification algorithm

CLC Number: 

  • TP183
[1] ZHONG Y F, FFENG F, ZHANG L P. Large patch convolutional neural networks for the scene classification of high spatial resolution imagery[J]. Journal of Applied Remote Sensing, 2016, 10(2): 025006.
[2] MASSON G, YANG D, et al. Version and vergence eye movements in humans: open-loop dynamics determined by monocular rather than binocular image speed[J]. Vision Research, 2002, 42(26): 2853-2867.
[3] SHOTTON J, FITZGIBBON A, COOK M, et al. Real-time human pose recognition in parts from single depth images[J]. Communications of the ACM, 2013, 56(1): 116-124.
[4] KANEKO A M, YAMAMOTO K. Two-view monocular depth estimation by optic-flow-weighted fusion[J]. IEEE Robotics and Automation Letters, 2019, 4(2): 830-837.
[5] AURISANO A, RADOVIC A, ROCCO D, et al. A convolutional neural network neutrino event classifier[J]. Journal of Instrumentation, 2016, 11(9): P09001.
[6] LIU F, SHEN C, LIN G. Deep convolutional neural fields for depth estimation from a single image[C] //Computer Vision and Pattern Recognition(CVPR). Boston, USA: IEEE Computer Society, 2015: 5162-5170.
[7] BO L, DAI Y, HE M. Monocular depth estimation with hierarchical fusion of dilated CNNs and soft-weighted-sum inference[J]. Pattern Recognition, 2017, 83: 328-339.
[8] GRIGOREV A, JIANG F, RHO S, et al. Depth estimation from single monocular images using deep hybrid network[J]. Multimedia Tools and Applications, 2017, 76(18): 18585-18604.
[9] DAN X, RICCI E, OUYANG W, et al. Monocular depth estimation using multi-scale continuous CRFs as sequential deep networks[J]. IEEE Transactions on Pattern Analysis & Machine Intelligence, 2019, 41(6): 1426-1440.
[10] LI J, KLEIN R, YAO A, et al. A two-streamed network for estimating fine-scaled depth maps from single RGB images[J]. Computer Vision and Image Understanding, 2019, 186:25-36.
[11] WANG X, HOU C, PU L, et al. A depth estimating method from a single image using FoE CRF[J]. Multimedia Tools & Applications, 2015, 74(21): 9491-9506.
[12] XU H, JIANG M, LI F. Depth estimation algorithm based on data-driven approach and depth cues for stereo conversion in three-dimensional displays[J]. Optical Engineering, 2016, 55(12): 12106-1-12106-11.
[13] LI B, DAI Y, HE M. Monocular depth estimation with hierarchical fusion of dilated CNNs and soft-weighted-sum inference[J]. Pattern Recognition, 2018, 83: 328-339.
[14] CHEN L, PAPANDREOU G, KOKKINOS I, et al. Semantic image segmentation with deep convolutional nets and fully connected CRFs[J]. Computer Science, 2018, 40(4): 357-361.
[15] CAO Y, SHEN C, SHEN H. Exploiting depth from single monocular images for object detection and semantic segmentation[J]. IEEE Transactions on Image Processing, 2017, 26(2): 836-846.
[16] CAO Y, WU Z, SHEN C.Estimating depth from monocular images as classification using deep fully convolutional residual Networks[J]. IEEE Transactions on Circuits and Systems for Video Technology, 2016, 38(10): 1-11.
[17] CHOI S, MIN D, HAM B, et al. Depth analogy: data-driven approach for single image depth estimation using gradient samples[J]. IEEE Transactions on Image Processing, 2015, 24(12): 5953-5966.
[18] QIN H, LI X, WANG Y, et al. Depth estimation by parameter transfer with a lightweight model for single still images[J]. IEEE Transactions on Circuits & Systems for Video Technology, 2017, 27(4): 748-759.
[19] 覃勋辉,马戎.一种基于梯度的直线段检测算法[J].光子学报, 2012,41(2):205-209. RONG X, MA R. A line segments detection algorithm based on grad[J]. Photonics Journal, 2012, 41(2): 205-209.
[20] GIOI R, MOREL J,et al. LSD: a fast line segment detector with a false detection control[J]. IEEE Transactions on Software Engineering, 2010, 32(4): 722-732.
[21] PATON K. Line detection by local methods[J]. Computer Graphics and Image Processing, 1979, 9(4): 316-332.
[22] BACON J, KING-SMITH P E. The detection of line segments[J]. Perception, 1977, 6(2): 125-131.
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