Quasi-Homography Warps in Image Stitching

Li, Nan; Xu, Yaming; Wang, Chao

doi:10.1109/tmm.2017.2771566

Cited by 100 publications

(57 citation statements)

References 46 publications

(104 reference statements)

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“…Nan Li et.al. developed Quasi homography warp that overcomes the problem of projective and perspective distortion by using local consistent scale linearization and global consistent slope preservation respectively [17].…”

Section: Full and Reduced Reference Image Quality Assessment Of Panormentioning

confidence: 99%

Full and Reduced Reference Image Quality Assessment of Panoramic View using Novel Hybrid Image Stitching Method

Vaidya*,

Gandhe

2020

IJITEE

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Image Stitching is becoming more popular in field of computer vision because of rapid development of efficient algorithms that replaces the high cost wider lens cameras and commercial image stitching tools. The existing methods used global geometric transformation in registration stage and hence suffered from object deformation, parallax error, ghosting effect and motion blur in output result. In this paper, newly developed Hybrid Warping of weighted linearized homography matrix and similarity transform matrix is implemented over standard image stitching database. The visual quality of stitched image using proposed method has been examined in terms of performance metrics of Full Reference Image Quality Assessment (FRIQA) such as Root Mean Square Error (RMSE), Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index (SSIM) and Reduced Reference Image Quality Assessment (RRIQA). Also, the performance analysis of proposed method is compared against existing image stitching methods in terms of field of view and stitching time. This analysis has ascertained the outperformance of Novel Hybrid Image Stitching method.

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Section: Full and Reduced Reference Image Quality Assessment Of Panormentioning

confidence: 99%

Full and Reduced Reference Image Quality Assessment of Panoramic View using Novel Hybrid Image Stitching Method

Vaidya*,

Gandhe

2020

IJITEE

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“…Because the similarity warp preserves individual perspectives, most of the stitching results are multiple-perspective, therefore they suffer from perspective inconsistency (see Figure 1(a)). An exception is [13] that uses a quasi-homography warp as the warp with less distortion. * (a) Multiple-perspective (GSP [14]).…”

Section: Introductionmentioning

confidence: 99%

Single-Perspective Warps in Natural Image Stitching

Liao

2020

IEEE Trans. on Image Process.

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108

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Results of image stitching can be perceptually divided into single-perspective and multiple-perspective. Compared to the multiple-perspective result, the single-perspective result excels in perspective consistency but suffers from projective distortion. In this paper, we propose two single-perspective warps for natural image stitching. The first one is a parametric warp, which is a combination of the as-projective-as-possible warp and the quasi-homography warp via dual-feature. The second one is a mesh-based warp, which is determined by optimizing a total energy function that simultaneously emphasizes different characteristics of the single-perspective warp, including alignment, naturalness, distortion and saliency. A comprehensive evaluation demonstrates that the proposed warp outperforms some state-ofthe-art warps, including homography, APAP, AutoStitch, SPHP and GSP.

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“…This interactive property enables users to feel like being in a virtual world. It gives rise to various new challenges at the same time, such as video/image production [1], [2], [3], transmission [4], [5], compression [6], [7], [8], and quality assessments [9], [10], [11]. Those new challenges are dissimilar to the cases in 2D traditional media since users can actively select the content they would like to watch with HMDs, while they are only allowed to passively receive the given content in 2D traditional video.…”

Section: Introductionmentioning

confidence: 99%

A Multi-FoV Viewport-Based Visual Saliency Model Using Adaptive Weighting Losses for 360$^\circ$ Images

Chao

Zhang

Hamidouche

et al. 2021

IEEE Trans. Multimedia

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• media allows observers to explore the scene in all directions. The consequence is that the human visual attention is guided by not only the perceived area in the viewport but also the overall content in 360 •. In this paper, we propose a method to estimate the 360 • saliency map which extracts salient features from the entire 360 • image in each viewport in three different Field of Views (FoVs). Our model is first pretrained with a largescale 2D image dataset to enable the interpretation of semantic contents, then fine-tuned with a relative small 360 • image dataset. A novel weighting loss function attached with stretch weighted maps is introduced to adaptively weight the losses of three evaluation metrics and attenuate the impact of stretched regions in equirectangular projection during training process. Experimental results demonstrate that our model achieves better performance with the integration of three FoVs and its diverse viewport images. Results also show that the adaptive weighting losses and stretch weighted maps effectively enhance the evaluation scores compared to the fixed weighting losses solutions. Comparing to other state of the art models, our method surpasses them on three different datasets and ranks the top using 5 performance evaluation metrics on the Salient360! benchmark set.

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Quasi-Homography Warps in Image Stitching

Cited by 100 publications

References 46 publications

Full and Reduced Reference Image Quality Assessment of Panoramic View using Novel Hybrid Image Stitching Method

Full and Reduced Reference Image Quality Assessment of Panoramic View using Novel Hybrid Image Stitching Method

Single-Perspective Warps in Natural Image Stitching

A Multi-FoV Viewport-Based Visual Saliency Model Using Adaptive Weighting Losses for 360$^\circ$ Images

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