Quantitative Assessment Method of Image Stitching Performance Based on Estimation of Planar Parallax

Jung, Kyunghwa; Hong, Jin

doi:10.1109/access.2020.3048759

Cited by 15 publications

(11 citation statements)

References 43 publications

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“…Image stitching technology is widely used for photo editing processes that contain certain camera features. Expensive cameras and lenses are not needed for image stitching technology that improves the cost-effectiveness of the system [2]. Image stitching technology provides various sets of services to create panoramic images for an application.…”

Section: Introductionmentioning

confidence: 99%

Image Stitching Technology in 3D Film Production Based on Digital Image Processing

Peng¹,

Yu²

2022

International Transactions on Electrical Energy Systems

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Image stitching in 3D film production synchronizes multiple independent frame images as a single high-resolution output. The stitching process is common for displaying a large coverage or shot in a single frame containing distinct images. For improving the realistic accuracy of 3D film images, this manuscript introduces an Itinerant Pixel-Matching-based Stitching Process (IPMSP). The proposed stitching process relies on cross-sectional pixels identified in merging two or more images. Based on the linear cumulative distribution in augmenting images, the homogeneity feature is identified. If the homogeneity is high then the stitching for linear pixels occurs increasing the frame resolution and size. The cumulative distribution is determined using the recurrent neural network in verifying homogeneity and contrast. For the contrast pixels identified, the cross-sectional matching is performed for substituting similar pixels in the missing sections. The process is repeated until the stitching region is the same based on the homogeneity feature and increased dimensions. Therefore, this process is capable of improving accuracy, precision, substitution, and reducing errors and complexity.

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Section: Introductionmentioning

confidence: 99%

Image Stitching Technology in 3D Film Production Based on Digital Image Processing

Peng¹,

Yu²

2022

International Transactions on Electrical Energy Systems

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“…One of the potential directions of further research might be the application of the proposed approach for quality assessment of parallax-tolerant image stitching methods [24] as well as the validation of the proposed approach for some other types of images and video sequences, containing similar types of distortions, also using some other combination models.…”

Section: Discussionmentioning

confidence: 99%

Entropy-Based Combined Metric for Automatic Objective Quality Assessment of Stitched Panoramic Images

Okarma

Chlewicki

Kopytek

et al. 2021

Entropy

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Quality assessment of stitched images is an important element of many virtual reality and remote sensing applications where the panoramic images may be used as a background as well as for navigation purposes. The quality of stitched images may be decreased by several factors, including geometric distortions, ghosting, blurring, and color distortions. Nevertheless, the specificity of such distortions is different than those typical for general-purpose image quality assessment. Therefore, the necessity of the development of new objective image quality metrics for such type of emerging applications becomes obvious. The method proposed in the paper is based on the combination of features used in some recently proposed metrics with the results of the local and global image entropy analysis. The results obtained applying the proposed combined metric have been verified using the ISIQA database, containing 264 stitched images of 26 scenes together with the respective subjective Mean Opinion Scores, leading to a significant increase of its correlation with subjective evaluation results.

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“…For example, in robotics [2,29], the height of points from the ground plane is crucial. Moreover, Jung et al [20] propose a method based on planar parallax for quantifying image stitching, and Vaish et al [48] use it in camera calibration. A critical problem in these methods is finding a suitable reference plane.…”

Section: Planar Parallax Methodsmentioning

confidence: 99%

Deep Planar Parallax for Monocular Depth Estimation

Liang¹,

Li²,

Yang³

et al. 2023

Preprint

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Depth estimation is a fundamental problem in the perception system of autonomous driving scenes. Although autonomous driving is challenging, much prior knowledge can still be utilized, by which the sophistication of the problem can be effectively restricted. Some previous works introduce the road plane prior to the depth estimation problem according to the Planar Parallax Geometry. However, we find that their usages are not effective, leaving the network cannot learn the geometric information. To this end, we analyze this problem in detail and reveal that explicit warping of consecutive frames and flow pre-training can effectively bring the geometric prior into learning. Furthermore, we propose Planar Position Embedding to deal with the intrinsic weakness of plane parallax geometry. Comprehensive experimental results on autonomous driving datasets like KITTI and Waymo Open Dataset (WOD) demonstrate that our Planar Parallax Network(PPNet) dramatically outperforms existing learning-based methods.

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Quantitative Assessment Method of Image Stitching Performance Based on Estimation of Planar Parallax

Cited by 15 publications

References 43 publications

Image Stitching Technology in 3D Film Production Based on Digital Image Processing

Image Stitching Technology in 3D Film Production Based on Digital Image Processing

Entropy-Based Combined Metric for Automatic Objective Quality Assessment of Stitched Panoramic Images

Deep Planar Parallax for Monocular Depth Estimation

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