Robust Alignment for Panoramic Stitching Via an Exact Rank Constraint

Li, Yuelong; Tofighi, Mohammad; Monga, Vishal

doi:10.1109/tip.2019.2909800

Cited by 15 publications

(4 citation statements)

References 56 publications

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“…However, their method can only handle small parallax. Li et al [23] presented bundle robust alignment model for stitching images based on pixels directly. Li et al [24] used triangular facet approximation method to align images.…”

Section: Traditional Techniquesmentioning

confidence: 99%

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Artefact‐free image stitching via a better normed seam‐cutting energy function

Qiu

2020

IET Image Processing

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Image stitching, as the important field of computer graphics and vision, has received much attention in recent years. Image stitching techniques are generally decomposed into two phases: image alignment, which aligns target images with the reference images; and image composition, which fixes ghosting and visual artefacts. This work aims to propose a new strategy for the seam‐cutting method which provides visually appealing result. Seam‐cutting is one of the most influential methods in image composition, which can relieve artefacts and produce plausible results. However, it is observed that the state‐of‐the‐art seam‐cutting approaches usually lead to undesirable seams in some challenging scenes. Here, the authors put forward a novel seam‐cutting method by defining a new energy function. This method uses 5/2 power of L1 norm as a colour difference which can magnify the weight of colour distinction to avoid undesirable seams and artefacts. The proposed method can be easily implemented. The test images are collected from the public available challenging datasets and taken by ourselves. Experiments demonstrate that the proposed method can create comparable or even better stitching results compared to other state‐of‐the‐art seam‐cutting approaches.

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Section: Traditional Techniquesmentioning

confidence: 99%

“…Li et al. [23] presented bundle robust alignment model for stitching images based on pixels directly. Li et al.…”

Section: Related Workmentioning

confidence: 99%

Artefact‐free image stitching via a better normed seam‐cutting energy function

Qiu

2020

IET Image Processing

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“…Today, images captured from various viewpoints or equipment, including remote sensing satellites, can display considerable geometric and photometric variances caused by factors like fluctuating environmental conditions, dissimilarities in camera technology, and unstable shooting scenarios. This difference makes combining or comparing these images difficult, affecting many applications such as augmented reality [1][2][3], object recognition [4,5], and panoramic stitching [6][7][8]. To solve this problem, researchers have employed various image processing techniques such as image registration [9,10], color correction [11,12], and homography estimation [13].…”

Section: Introductionmentioning

confidence: 99%

A Review of Homography Estimation: Advances and Challenges

Luo,

Wang,

Liao

et al. 2023

Electronics

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Images captured from different viewpoints or devices have often exhibited significant geometric and photometric differences due to factors such as environmental variations, camera technology differences, and shooting conditions’ instability. To address this problem, homography estimation has attracted much attention as a method to describe the geometric projection relationship between images. Researchers have proposed numerous homography estimation methods for single-source and multimodal images in the past decades. However, the comprehensive review and analysis of homography estimation methods, from feature-based to deep learning-based, is still lacking. Therefore, we provide a comprehensive overview of research advances in homography estimation methods. First, we provide a detailed introduction to homography estimation’s core principles and matrix representations. Then, we review homography estimation methods for single-source and multimodal images, from feature-based to deep learning-based methods. Specifically, we analyze traditional and learning-based methods for feature-based homography estimation methods in detail. For deep learning-based homography estimation methods, we explore supervised, unsupervised, and other methods in-depth. Subsequently, we specifically review several metrics used to evaluate these methods. After that, we analyze the relevant applications of homography estimation and show the broad application prospects of this technique. Finally, we discuss current challenges and future research directions, providing a reference for computer vision researchers and engineers.

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“…Searching for image matching pairs is computationally expensive and consumes a lot of time. Image stitching technology mainly includes two key parts: image registration and image fusion [3]. Image registration, the process of aligning two or more images, is the core technique of many images and videos analysis tasks.…”

Section: Introductionmentioning

confidence: 99%

Unordered Image Stitching Based on the Registration Index Table

2022

J. Phys.: Conf. Ser.

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In order to solve the problem of large amount of calculation and time-consuming in the process of unordered image stitching, we propose an image stitching algorithm based on the image registration index table in this paper. Firstly, we construct the matching index sequence based on the similarity of the matching area. Then we search for image matching pairs according to the matching index sequence, carry out image registration and stitch directly, and generate the registration index table. For image stitching, we build a binary tree model assisted by the image registration index table, we need to search for matching pairs and carry out registration and stitching according to the registration index table generated by each layer. Compare to the current research on disordered image stitching, our method significantly reduces the times of feature extraction and matching by at least half, therefore, it greatly improves the efficiency of image stitching.

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Robust Alignment for Panoramic Stitching Via an Exact Rank Constraint

Cited by 15 publications

References 56 publications

Artefact‐free image stitching via a better normed seam‐cutting energy function

Artefact‐free image stitching via a better normed seam‐cutting energy function

A Review of Homography Estimation: Advances and Challenges

Unordered Image Stitching Based on the Registration Index Table

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