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. © 2013 Springer-Verlag Berlin Heidelberg.
CITATION STYLE
Mo, Y., Liu, T., Zhu, X., Dai, X., & Luo, J. (2013). Segment based depth extraction approach for monocular image with linear perspective. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 8261 LNCS, pp. 168–175). Springer Verlag. https://doi.org/10.1007/978-3-642-42057-3_22
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