Depth manipulation using disparity histogram analysis for stereoscopic 3D

Lee, Sang‐Woo; Kim, Younghui; Lee, Jungjin; Kim, Kye Hyun; Lee, Kyunghan; Noh, Junyong

doi:10.1007/s00371-013-0868-3

Cited by 12 publications

(5 citation statements)

References 12 publications

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“…We want the salient regions of the scene to occupy a larger share of the depth range while compressing non-salient regions and artifacts. An analogous problem arises in stereo viewing and relief mapping, where disparity or depth compression is needed [22,27,30,40,54]. In KDI, compression requires global consistency of the scene depth as the depth mesh is viewed from different angles; otherwise, the depth inconsistencies will be perceived easily.…”

Section: Optimized Depth Compressionmentioning

confidence: 98%

Kinetic depth images: flexible generation of depth perception

2016

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In this paper we present a systematic approach to create smoothly varying images from a pair of photographs to facilitate enhanced awareness of the depth structure of a given scene. Since our system does not rely on sophisticated display technologies such as stereoscopy or auto-stereoscopy for depth awareness, it (a) is inexpensive and widely accessible, (b) does not suffer from vergence -accommodation fatigue, and (c) works entirely with monocular depth cues. Our approach enhances the depth awareness by optimizing across a number of features such as depth perception, optical flow, saliency, centrality, and disocclusion artifacts. We report the results of user studies that examine the relationship between depth perception, relative velocity, spatial perspective effects, and the positioning of the pivot point and use them when generating kinetic-depth images. We also present a novel depth re-mapping method guided by perceptual relationships based on the results of our user study. We validate our system by presenting a user study that compares the output quality of our proposed method against other existing alternatives on a wide range of images.

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Section: Optimized Depth Compressionmentioning

confidence: 98%

Kinetic depth images: flexible generation of depth perception

2016

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“…A depth manipulation method by using disparity histogram analysis was presented in [12]. This approach treats disparity remapping as a layer-based issue through analyzing the disparity maps and using nonlinear disparity adjustment functions.…”

Section: Related Workmentioning

confidence: 99%

“…This kind of methods can prevent results from creating new occlusions and is always robust, but it can not completely avoid distortion in some regions, especially in object edges. For other works [7] [9] [12], they addressed this issue by using the estimated disparity maps or user interaction. These methods can minimize distortion , but the computational cost is very high or the application field is limited.…”

Section: Introductionmentioning

confidence: 99%

Patch-Based Disparity Remapping for Stereoscopic Images

Liu

et al. 2015

MultiMedia Modeling

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Abstract. Post-production and processing for stereoscopic 3D are attracting a lot attention in recent years. In particular, the acquired disparity in most situations requires further manipulation to adjust different view conditions. This paper proposes a novel method to address the issue of disparity remapping of stereoscopic images. We present a nonlinear disparity mapping model to adjust the depth range of the whole image as well as the special visual important regions. To implement this model, our method compute saliency maps for the stereoscopic images. Then we extend the PatchMatch algorithm to search for the proper patches in both the left and the right images by visual combined constraints, and use them to iteratively refine the images to meet the target depth range. Our method is capable of minimizing the distortion of the images and ensuring the correct stereo consistency after disparity remapping. The experimental results demonstrate that the proposed approach can adjust the depth range to improve the stereoscopic effects while preserving the naturalness of the scene.

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“…Chang et al [2] and Liu et al [17] proposed the content-aware stereoscopic image and video resizing methods, respectively, which can adapt images and videos to different target display devices and adjust the perceived depth in a visual comfort zone. Lee et al [12] proposed a nonlinear disparity remapping system to make the depth manipulation more intuitive and effective. Wang et al [25] presented a unified stereoscopic video disparity adjustment framework to automatically improve the 3D viewing experience.…”

Section: Stereoscopic Image and Video Editingmentioning

confidence: 99%

Stereoscopic image stitching with rectangular boundaries

2019

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This paper proposes a novel algorithm for stereoscopic image stitching, which aims to produce stereoscopic panoramas with rectangular boundaries. As a result, it provides wider field of view and better viewing experience for users. To achieve this, we formulate stereoscopic image stitching and boundary rectangling in a global optimization framework that simultaneously handles feature alignment, disparity consistency and boundary regularity. Given two (or more) stereoscopic images with overlapping content, each containing two views (for left and right eyes), we represent each view using a mesh and our algorithm contains three main steps: We first perform a global optimization to stitch all the left views and right views simultaneously, which ensures feature alignment and disparity consistency. Then, with the optimized vertices in each view, we extract the irregular boundary in the stereoscopic panorama, by performing polygon Boolean operations in left and right views, and construct the rectangular boundary constraints. Finally, through a global energy optimization, we warp left and right views according to feature alignment, disparity consistency and rectangular boundary constraints. To show the effectiveness of our method, we further extend our method to disparity adjustment and stereoscopic stitching with large horizon. Experimental results show that our method can produce visually pleasing stereoscopic panoramas without noticeable distortion or visual fatigue, thus resulting in satisfactory 3D viewing experience.

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Depth manipulation using disparity histogram analysis for stereoscopic 3D

Cited by 12 publications

References 12 publications

Kinetic depth images: flexible generation of depth perception

Kinetic depth images: flexible generation of depth perception

Patch-Based Disparity Remapping for Stereoscopic Images

Stereoscopic image stitching with rectangular boundaries

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