360 Stitching from Dual-Fisheye Cameras Based on Feature Cluster Matching

Souza, Tancredo; Roberto, Rafael; Lima, João Paulo; Teichrieb, Verônica; Quintino, Jonysberg P.; Silva, Fábio; Santos, André L. M.; Pinho, Helder

doi:10.1109/sibgrapi.2018.00047

Cited by 3 publications

(2 citation statements)

References 11 publications

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“…3 However, VR is being used for sports broadcasting and live performances through killer content in many ways. 4 To show highresolution images such as sports broadcasting, multiple images must be accurately stitched. The performance of image stitching, the core technology of VR, is important to create seamless, 360-panorama images similar to the real world.…”

Section: Introductionmentioning

confidence: 99%

“…Image stitching is a method for matching multiple images to produce a high-resolution image and a wide field of view image and it has been studied to develop various algorithms over the last few years. [4][5][6][7][8][9][10][11][12]13 A technology that combines multiple images to produce high resolution and wide field of view images is commonly used in the production of special image mosaics, especially panoramic images. Stitched images are used in applications such as interactive panoramic viewing of images, architectural walk-through, multi-node movies, and other applications associated with modeling the 3D environment using images acquired from the real world.…”

Section: Introductionmentioning

confidence: 99%

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Improved method on image stitching based on optical flow algorithm

Lee¹,

Lee

Choi

2020

International Journal of Engineering Business Management

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With the rapid development of technologies based on virtual reality, image stitching is widely used in various fields such as broadcasting, games, education, and architecture. Image stitching is a method for connecting multiple images to produce a high-resolution image and a wide field of view image. It is common for most of the stitching methods to find and match the feature in the image. However, these stitching methods have the disadvantage that they cannot create a perfect 360-degree panoramic image because the depth of the projected area varies depending on the position and direction between adjacent cameras. Therefore, we propose an advanced stitching method to improve the deviation due to the difference in the depth of each area using the pixel value of the input image after the feature-based stitching. After the feature-based stitching method has been performed, the pixel values of overlapping areas in the image are calculated as an optical flow algorithm, then finely distorted, and then corrected to ensure that the image overlaps correctly. Through experiments, it was confirmed that the problem that was deviated from the feature-based stitching was solved. Besides, as a result of performance evaluation, it was proved that the proposed stitching method using an optical flow algorithm is capable of real-time and fast service.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Improved method on image stitching based on optical flow algorithm

Lee¹,

Lee

Choi

2020

International Journal of Engineering Business Management

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Dual-Fisheye Image Stitching via Unsupervised Deep Learning

Jin,

Dong,

et al. 2024

Lecture Notes in Computer Science

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Generating a full spherical view by modeling the relation between two fisheye images

Flores,

Valiente,

Peidró

et al. 2024

Vis Comput

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Full spherical views provide advantages in many applications that use visual information. Dual back-to-back fisheye cameras are receiving much attention to obtain this type of view. However, obtaining a high-quality full spherical view is very challenging. In this paper, we propose a correction step that models the relation between the pixels of the pair of fisheye images in polar coordinates. This correction is implemented during the mapping from the unit sphere to the fisheye image using the equidistant fisheye projection. The objective is that the projections of the same point in the pair of images have the same position on the unit sphere after the correction. In this way, they will also have the same position on the equirectangular coordinate system. Consequently, the discontinuity between the spherical views for blending is minimized. Throughout the manuscript, we show that the angular polar coordinates of the same scene point in the fisheye images are related by a sine function and the radial distance coordinates by a linear function. Also, we propose employing a polynomial as a geometric transformation between the pair of spherical views during the image alignment since the relationship between the matching points of pairs of spherical views is not linear, especially in the top/bottom regions. Quantitative evaluations demonstrate that using the correction step improves the quality of the full spherical view, i.e. IQ MS-SSIM, up to 7%. Similarly, using a polynomial improves the IQ MS-SSIM up to 6.29% with respect to using an affine matrix.

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360 Stitching from Dual-Fisheye Cameras Based on Feature Cluster Matching

Cited by 3 publications

References 11 publications

Improved method on image stitching based on optical flow algorithm

Improved method on image stitching based on optical flow algorithm

Dual-Fisheye Image Stitching via Unsupervised Deep Learning

Generating a full spherical view by modeling the relation between two fisheye images

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