Abstract
In this paper, a segment-guided depth extraction approach is proposed for monocular image with linear perspective. Firstly, foreground depth is learned from a RGBD database with segment-based calibration to adjust the initial coarse depth, and background depth is estimated from linear perspective by vanishing cues. Then, the foreground depth and background one are linearly combined with a statistically optimal balance factor to obtain a holistic fused depth map. Lastly, bilateral filter is exploited to suppress the depth disturbance with edge-preserving. Experiments demonstrate that the proposed technique can produce accurate and dense depths with distinct object boundaries and correct relation among the object positions for a single image.
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References
Cheng, C.M., Hsu, X.A., Lai, S.H.: A Novel Structure-from-Motion Strategy for Refining Depth Map Estimation and Multi-view Synthesis in 3DTV. In: IEEE International Conference on Multimedia and Expo, pp. 944–949. IEEE press, Suntec City (2010)
Favaro, P.: Recovering Thin Structures via Nonlocal-means Regularization with Application to Depth from Defocus. In: IEEE International Conference on Computer Vision and Pattern Recognition, pp. 1133–1140. IEEE press, San Francisco (2010)
Saxena, A., Sun, M., Ng, A.Y.: Make3D: Learning 3D Scene Structure from Single Still Image. IEEE Trans. Pattern Anal. Mach. Intell. 31(5), 824–840 (2009)
Karsch, K., Liu, C., Kang, S.B.: Depth Extraction from Video Using Non-parametric Sampling. In: Fitzgibbon, A., Lazebnik, S., Perona, P., Sato, Y., Schmid, C. (eds.) ECCV 2012, Part V. LNCS, vol. 7576, pp. 775–788. Springer, Heidelberg (2012)
Um, G.M., Bang, G., Cheong, W.S., Hur, N., Lee, S.I.: Improvement of Segment-based Depth Estimation using a Novel Segment Extraction. In: 3DTV Conf., Tampere, pp. 1–4 (2010)
Lai, Y.K., Lai, Y.F., Chen, Y.C.: An Effective Hybrid Depth-generation Algorithm for 2D-to-3D Conversion in 3D Displays. J. Disp. Technol. 9(3), 154–161 (2013)
Saxena, A., Chung, S.H., Ng, A.Y.: Learning Depth from Single Monocular Images. J. Adv. Neural Inf. Process. Syst. 18, 1161 (2006)
Dalal, N., Triggs, B.: Histograms of Oriented Gradients for Human Detection. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 886–893. IEEE press, Can Diego (2005)
Oliva, A., Torralba, A.: Building the Gist of a Scene: The Role of Global Image Features in Recognition. Prog. Brain Res. 155, 23–36 (2006)
Liu, C., Yuen, J., Antonio, A.B.: SIFT Flow: Dense correspondence across scenes and its applications. IEEE Trans. Pattern Anal. Mach. Intell. 33(5), 978–994 (2011)
Criminisi, A., Reid, I., Zisserman, A.: Efficient graph-based image segmentation. Int. J. Comput. Vision. 59(2), 167–181 (2004)
Schmitt, F., Priese, L.: Vanishing Point Detection with an Intersection Point Neighborhood. In: Brlek, S., Reutenauer, C., Provençal, X. (eds.) DGCI 2009. LNCS, vol. 5810, pp. 132–143. Springer, Heidelberg (2009)
Tian, C., Krishnan, S.: Accelerated Bilateral Filtering with Block Skipping. IEEE Signal Process. Lett. 20(5), 419–422 (2013)
Hore, A., Ziou, D.: Image Quality Metrics: PSNR vs. SSIM. In: 20th International Conference on Pattern Recognition, pp. 2366–2369. IEEE press, Istanbul (2010)
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Mo, Y., Liu, T., Zhu, X., Dai, X., Luo, J. (2013). Segment Based Depth Extraction Approach for Monocular Image with Linear Perspective. In: Sun, C., Fang, F., Zhou, ZH., Yang, W., Liu, ZY. (eds) Intelligence Science and Big Data Engineering. IScIDE 2013. Lecture Notes in Computer Science, vol 8261. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-42057-3_22
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DOI: https://doi.org/10.1007/978-3-642-42057-3_22
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