Shape-Preserving Stereo Object Remapping via Object-Consistent Grid Warping

Bing Li, Chia-Wen Lin, Cheng Zheng, Shan Liu, Bernard Ghanem, Wen Gao, C.-C. Jay Kuo

Research output: Contribution to journalArticlepeer-review

Abstract

Viewing various stereo images under different viewing conditions has escalated the need for effective object-level remapping techniques. In this paper, we propose a new object spatial mapping scheme, which adjusts the depth and size of the selected object to match user preference and viewing conditions. Existing warping-based methods often distort the shape of important objects or cannot faithfully adjust the depth/size of the selected object due to improper warping such as local rotations. In this paper, by explicitly reducing the transformation freedom degree of warping, we propose an optimization model based on axis-aligned warping for object spatial remapping. The proposed axis-aligned warping based optimization model can simultaneously adjust the depths and sizes of selected objects to their target values without introducing severe shape distortions. Moreover, we propose object consistency constraints to ensure the size/shape of parts inside a selected object to be consistently adjusted. Such constraints improve the size/shape adjustment performance while remaining robust to some extent to incomplete object extraction. Experimental results demonstrate that the proposed method achieves high flexibility and effectiveness in adjusting the size and depth of objects compared with existing methods.
Original languageEnglish (US)
Pages (from-to)1-1
Number of pages1
JournalIEEE Transactions on Image Processing
DOIs
StatePublished - 2021

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