Feature-based image stitching algorithms

Bonny, Moushumi Zaman; Uddin, Mohammad Shorif

doi:10.1109/iwci.2016.7860365

Cited by 27 publications

(15 citation statements)

References 20 publications

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“…RAN SAC is a robust technique used to estimate the Homography and remove outlier points randomly from images to provide good matches [11] and increase quality [2]. RAN SAC randomly select a set of data required to calculate a mathematical model of data parameters [26] [16].…”

Section: E Ransacmentioning

confidence: 99%

“…Then, with an effective random sample [16], RAN SAC uses a small number of points to estimate the model and check if it agrees with the remaining points by calculating their distance to the fitted model. RAN SAC can be performed N times until a subset of the image is found with a good matching relationship [11].…”

Section: E Ransacmentioning

confidence: 99%

“…Yu and McMillan presented a model that describes a multi-perspective camera [10]. Panoramic image stitching is used in a variety of environment, including gaming, virtual reality, virtual museums, and map applications [11]. Microsoft Research, for example, is spending on research projects featuring panorama stitching techniques, and many algorithms are designed to efficiently facilitating the creation of panoramic images through stitching [12] [13].…”

Section: Introductionmentioning

confidence: 99%

“…Most researchers classify panorama stitching as either a direct technique or a feature-based technique [11] [14]. The direct technique compares pixel to pixel between images and the feature-based technique compares all features within each image [14].…”

Section: Introductionmentioning

confidence: 99%

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Creating See-Around Scenes using Panorama Stitching

Alferidah

Alkhaldi

2019

Proceedings of the 2019 Federated Conference on Computer Science and Information Systems

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Image stitching refers to the process of combining multiple images of the same scene to produce a single highresolution image, known as panorama stitching. The aim of this paper is to produce a high-quality stitched panorama image with less computation time. This is achieved by proposing four combinations of algorithms. First combination includes FAST corner detector, Brute Force K-Nearest Neighbor (KNN) and Random Sample Consensus (RANSAC). Second combination includes FAST, Brute Force (KNN) and Progressive Sample Consensus (PROSAC). Third combination includes ORB, Brute Force (KNN) and RANSAC. Fourth combination contains ORB, Brute Force (KNN) and PROSAC. Next, each combination involves a calculation of Transformation Matrix. The results demonstrated that the fourth combination produced a panoramic image with the highest performance and better quality compared to other combinations. The processing time is reduced by 67% for the third combination and by 68% for the fourth combination compared to stat-of-the-art.

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Section: E Ransacmentioning

confidence: 99%

Section: E Ransacmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Creating See-Around Scenes using Panorama Stitching

Alferidah

Alkhaldi

2019

Proceedings of the 2019 Federated Conference on Computer Science and Information Systems

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“…Generally, a feature-based stitching method is used to solve the association between images based on features extracted from the input image. 16 However, many studies 11,13,17,18 have so far been conducted, but no complete stitching method has been found. The existing feature-based stitching method has the disadvantage of not being able to produce a more accurate and detailed 360-panorama because the depth of the projecting area and the center point position is different between cameras according to the position and direction between adjacent cameras.…”

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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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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