E-mail Alert
RSS
| Citation: |
Li Pengfei, Shao Feng. Stereoscopic color transfer and disparity remapping based on selected object[J]. Opto-Electronic Engineering, 2019, 46(9): 180446. doi: 10.12086/oee.2019.180446
|
Stereoscopic color transfer and disparity remapping based on selected object
-
Abstract
Color transfer has been a hot research issue in the field of image processing and computer vision in recent years. The main purpose is to transfer the color of a target image to source image so that the source image has the same or similar color features with the target image. In practical applications for the color transfer of binocular stereoscopic images, the user may only need to transfer the color of the selected object while keeping the background color unchanged. For this purpose, a color transfer method based on the selected object is proposed in this paper. In the method, by assigning the object of the image by user, the accurate object is segmented via graph cut, and the probability density curves of color distribution between the selected object and the target image are matched to accomplish the color transfer. In order to enhance the viewing experience provided for the user, a non-linear disparity optimization is performed after the color transfer operation. According to the histogram feature of disparity map, the disparity mapping function is calculated, and the target disparity is obtained to enhance the depth sensation of the selected object. The experimental results demonstrate that the combination of stereoscopic color transfer and disparity remapping effectively enhances the stereoscopic viewing experience. -
-
References
[1] Faridul H S, Pouli T, Chamaret C, et al. A survey of color mapping and its applications[C]//Eurographics 2014 State of the Art Reports, 2014. [2] Pouli T, Reinhard E. Progressive histogram reshaping for creative color transfer and tone reproduction[C]//Proceedings of the 8th International Symposium on Non-Photorealistic Animation and Rendering, 2010: 81–90. http://www.researchgate.net/publication/221523265_Progressive_histogram_reshaping_for_creative_color_transfer_and_tone_reproduction [3] Reinhard E, Adhikhmin M, Gooch B, et al. Color transfer between images[J]. IEEE Computer Graphics and Applications, 2001, 21(5): 34–41. [4] Pitie F, Kokaram A. The linear Monge-Kantorovitch linear colour mapping for example-based colour transfer[C]//Proceedings of the 4th European Conference on Visual Media Production, 2007. [5] Evans L C. Partial differential equations and monge-kantorovich mass transfer[J]. Current Developments in Mathematics, 1997, 1997: 65–126. doi: 10.4310/CDM.1997.v1997.n1.a2 [6] Hristova H, Le Meur O, Cozot R, et al. Style-aware robust color transfer[C]//Proceedings of the workshop on Computational Aesthetics, 2015. http://www.researchgate.net/publication/283206715_Style-aware_robust_color_transfer [7] Abadpour A, Kasaei S. A fast and efficient fuzzy color transfer method[C]//Proceedings of the 4th IEEE International Symposium on Signal Processing and Information Technology, 2004: 491–494. http://www.researchgate.net/publication/4145584_A_fast_and_efficient_fuzzy_color_transfer_method [8] Tai Y W, Jia J Y, Tang C K. Local color transfer via probabilistic segmentation by expectation-maximization[C]//Proceedings of 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2005: 747–754. http://www.researchgate.net/publication/4156245_Local_color_transfer_via_probabilistic_segmentation_by_expectation-maximization [9] Hristova H, Le Meur O, Cozot R, et al. Transformation of the beta distribution for color transfer[C]//Proceedings of the 13th International Conference on Computer Graphics Theory and Applications, 2018: 112–121. [10] Agrawal A, Raskar R. Gradient domain manipulation techniques in vision and graphics[C]//Proceedings of the 11th International Conference on Computer Vision, 2007. [11] Shao F, Lin W S, Li Z T, et al. Toward simultaneous visual comfort and depth sensation optimization for stereoscopic 3-D experience[J]. IEEE Transactions on Cybernetics, 2017, 47(12): 4521–4533. doi: 10.1109/TCYB.2016.2615856 [12] Pascal F, Bombrun L, Tourneret J Y, et al. Parameter estimation for multivariate generalized Gaussian distributions[J]. IEEE Transactions on Signal Processing, 2013, 61(23): 5960–5971. doi: 10.1109/TSP.2013.2282909 [13] Bombrun L, Pascal F, Tourneret J Y, et al. Performance of the maximum likelihood estimators for the parameters of multivariate generalized Gaussian distributions[C]//Proceedings of 2012 IEEE International Conference on Acoustics, Speech and Signal Processing, 2012: 3525–3528. [14] Hristova H, Le Meur O, Cozot R, et al. Transformation of the multivariate generalized Gaussian distribution for image editing[J]. IEEE Transactions on Visualization and Computer Graphics, 2018, 24(10): 2813–2826. doi: 10.1109/TVCG.2017.2769050 [15] 李冰飞.基于RGBD图像的图像分割算法研究[D].合肥: 合肥工业大学, 2016. Li B F. The Research on image segmentation algorithms based on RGBD images[D]. Hefei: Hefei University of Technology, 2016. http://cdmd.cnki.com.cn/Article/CDMD-10359-1016124428.htm [16] Lang M, Hornung A, Wang O, et al. Nonlinear disparity mapping for stereoscopic 3D[J]. ACM Transactions on Graphics, 2010, 29(4): 75. [17] Sun D Q, Roth S, Black M J. Secrets of optical flow estimation and their principles[C]//Proceedings of 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2010: 2432–2439. http://www.researchgate.net/publication/45174916_Secrets_of_optical_flow_estimation_and_their_principles [18] Shamir A, Sorkine O. Visual media retargeting[C]//ACM SIGGRAPH ASIA 2009 Courses, 2009: 1–13. http://www.researchgate.net/publication/220721568_Visual_media_retargeting [19] Pitié F, Kokaram A C, Dahyot R. Automated colour grading using colour distribution transfer[J]. Computer Vision and Image Understanding, 2007, 107(1–2): 123–137. doi: 10.1016/j.cviu.2006.11.011 [20] Pitie F, Kokaram A C, Dahyot R. N-dimensional probability density function transfer and its application to color transfer[C]//Proceedings of the 10th International Conference on Computer Vision, 2005: 1434–1439. http://www.researchgate.net/publication/4193994_N-dimensional_probability_density_function_transfer_and_its_application_to_color_transfer -
Overview
Overview: Color transfer is a hot research topic in the field of image processing and machine vision in recent years. It is a process of transferring the color of a target image to the source image so that the source image and the target image have the same or similar color characteristics. It has a wide range of application prospects and can be used for image color correction, re-rendering and artistic processing. The existing color transfer methods are to process only the 2D image, establish the source image and the target image color feature multivariate Gaussian model by parameter estimation, and get the source image color feature model by multivariate Gaussian function transformation to approach the color feature model of the target image, then finish the color transfer. With the development of stereoscopic image technology, the color transfer of stereoscopic images has attracted more and more attention. In this paper, a color transfer strategy for binocular stereoscopic images is proposed, which can improve the viewing experience of users while completing color transfer. According to the actual needs of users, we can only transfer the color of the target object, and keep the color of the background unchanged. In the proposed method, the user specifies the image object, and then uses the graph cut method to segment the image, according to the selected object and the color feature model matching of the target image to complete color transfer. In order to further enhance the viewing effect, this paper carries out nonlinear disparity optimization while color transfer, so as to improve the depth of the object. According to the histogram feature of disparity map, the region which has a great influence on the stereoscopic image is determined, and the disparity mapping function is calculated by integral operation, then the disparity of the selected object which makes the depth sense of the selected object more intense is obtained. In this paper, images are randomly selected from different stereo image databases and our experiment results have been compared with the linear disparity adjustment method. The results show that this method can improve the depth sense of the object more effectively. To prove the effect of this strategy, a subjective experiment is designed, in which different people are demanded to wear stereo glasses to choose the images they feel better. Experimental results show that the combination of color transfer and disparity optimization can effectively improve the viewing experience of stereoscopic images.
-
Access History
Export File
Citation
Li Pengfei, Shao Feng. Stereoscopic color transfer and disparity remapping based on selected object[J]. Opto-Electronic Engineering, 2019, 46(9): 180446. doi: 10.12086/oee.2019.180446
Format
Content
DownLoad:
-
Figure 1.
The proposed framework
-
Figure 2.
Graph cut method based on selected object.
-
Figure 3.
Color distribution transformation.
-
Figure 4.
Nonlinear disparity mapping.
-
Figure 5.
Nonlinear disparity mapping result.
-
Figure 6.
Color transfer based on selected objects.
-
Figure 7.
Results of the proposed method.
-
Figure 8.
Comparison of disparity optimization methods.
-
Figure 9.
Comparison of color transfer methods.
-
Figure 10.
Comparison between proposed method and the source image
-
Figure 11.
Comparison between proposed method and the linear disparity mapping
