An Efficient Depth Estimation Technique Using 3-Trait Luminance Profiling

I. Usman

Abstract


This paper presents an efficient depth estimation technique for depth image-based rendering process in the 3-D television system. It uses three depth cues, namely linear perspective, motion information, and texture characteristics, to estimate the depth of an image. In addition, suitable weights are assigned to different components of the image based on their relative perspective position of either the foreground or the background in the scene. Experimental results on publicly available datasets validate the usefulness of the proposed technique for efficient estimation of depth maps.


Keywords


depth estimation; 3D TV; DIBR; depth image; 3-D warping

Full Text:

PDF

References


J. Son, B. Javidi, S. Yano, K. Choi, “Recent Developments in 3-D Imaging Technologies”, Journal of Display Technology, Vol. 6, No. 10, pp. 394-403, 2010

L. Zhang, W. J. Tam, “Stereoscopic image generation based on depth images for 3D TV”, IEEE Transactions on Broadcasting, Vol. 51, No. 2, pp. 191-199, 2005

A. Almansa, A. Desolneux, S. Vamech, “Vanishing point detection without any a priori information”, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 25, No. 4, pp. 502-507, 2003

B. Wang, J. Zou, Y. Li, K. Ju, H. Xiong, Y. F. Zheng, “Sparse-to-Dense Depth Estimation in Videos via High-Dimensional Tensor Voting”, IEEE Transactions on Circuits and Systems for Video Technology, Vol. 29, No. 1, pp. 68-79, 2019

J. Liu, Y. Wang, Y. Li, J. Fu, J. Li, H. Lu, “Collaborative Deconvolutional Neural Networks for Joint Depth Estimation and Semantic Segmentation”, IEEE Transactions on Neural Networks and Learning Systems, Vol. 29, No. 11, pp. 5655-5666, 2018

Z. Hao, Y. Li, S. You, F. Lu, “Detail Preserving Depth Estimation from a Single Image Using Attention Guided Networks”, 2018 International Conference on 3D Vision (3DV), Verona, Italy, September 5-8, 2018

X. Jiang, M. L. Pendu, C. Guillemot, “Depth Estimation with Occlusion Handling from a Sparse Set of Light Field Views”, 25th IEEE International Conference on Image Processing, Athens, Greece, October 7-10, 2018

M. Carvalho, B. Le Saux, P. Trouve-Peloux, A. Almansa, F. Champagnat, “On Regression Losses for Deep Depth Estimation”, 25th IEEE International Conference on Image Processing, Athens, Greece, October 7-10, 2018

X. Duan, X. Ye, Y. Li, H. Li, “High Quality Depth Estimation from Monocular Images Based on Depth Prediction and Enhancement Sub-Networks”, IEEE International Conference on Multimedia and Expo, San Diego, USA, July 23-27, 2018

K. Ghosh, S. K. Pal, “Some Insights Into Brightness Perception of Images in the Light of a New Computational Model of Figure–Ground Segregation”, IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans, Vol. 40, No. 4, pp. 758-766, 2010

M. Song, D. Tao, C. Chen, X. Li, C. W. Chen, “Color to Gray: Visual Cue Preservation”, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 32, No. 9, pp. 1537-1552, 2010

A. V. Le, S. W. Jung, C. S. Won, “Directional Joint Bilateral Filter for Depth Images”, Sensors, Vol. 14, No. 7, pp. 11362-11378, 2014

http://vision.middlebury.edu/stereo/data/scenes2005/[Accessed: 21-Apr-2019]




eISSN: 1792-8036     pISSN: 2241-4487