Image Stitching based on Feature Extraction Techniques: A Survey

Adel, Ebtsam; Elmogy, Mohammed; El-Bakry, Hazem M.

doi:10.5120/17374-7818

Cited by 70 publications

(38 citation statements)

References 33 publications

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“…1). The first step aligns a set of images with a reference one, correcting perspective disparities that may cause parallax errors as described by (Adel, 2014). SIFT (Lowe, 2004) and SURF (Bay et al, 2008) are the * Also with LGO UMR 6538 CNRS UBO UBS most robust feature point extraction algorithms to scaling and affine distortions.…”

Section: Image Mosaicing: Principles and Methodsmentioning

confidence: 99%

An Advanced Benchmarking for Image Compositing Evaluation

Giudici¹,

Courtrai²,

Lefèvre³

2019

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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<p><strong>Abstract.</strong> This paper introduces a novel benchmarking tool for measuring the robustness of existing mosaicing algorithms in presence of a given set of disturbances. The process combines a set of partially overlapping images into a wide-view result used to represent UAV image series and orthophotography. Geometrical misalignements caused by perspective error may lead to some unpredictable artifacts and phantom effects in the mosaics. A very few solutions measure their immunity to known distortions and mainly focus on registration accuracy measurement. Only limited attention was given to characterize the response of the actual image fusion algorithms and their capacity to properly preserve content geometries. In this paper, we also introduce a new fidelity metric assessing the mosaicing response to a disturbance of a given extent used as prior information. The metric helps to better define the use cases fulfilling aerial imaging requirements.</p>

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Section: Image Mosaicing: Principles and Methodsmentioning

confidence: 99%

An Advanced Benchmarking for Image Compositing Evaluation

Giudici¹,

Courtrai²,

Lefèvre³

2019

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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“…(10), we set θ 1 =0.2, θ 2 =0.3 and θ 3 =0.5 empirically. Besides, D th and R th for solving (14) are set to be 0.4 and 2Mbps, respectively.…”

Section: B Comparison Of the Tile-based Bit Allocation Algorithmsmentioning

confidence: 99%

Spatial and Temporal Consistency-Aware Dynamic Adaptive Streaming for 360-Degree Videos

Yuan

Zhao

Hou

et al. 2020

IEEE J. Sel. Top. Signal Process.

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The 360-degree video allows users to enjoy the whole scene by interactively switching viewports. However, the huge data volume of the 360-degree video limits its remote applications via network. To provide high quality of experience (QoE) for remote web users, this paper presents a tile-based adaptive streaming method for 360-degree videos. First, we propose a simple yet effective rate adaptation algorithm to determine the requested bitrate for downloading the current video segment by considering the balance between the buffer length and video quality. Then, we propose to use a Gaussian model to predict the field of view at the beginning of each requested video segment. To deal with the circumstance that the view angle is switched during the display of a video segment, we propose to download all the tiles in the 360-degree video with different priorities based on a Zipf model. Finally, in order to allocate bitrates for all the tiles, a two-stage optimization algorithm is proposed to preserve the quality of tiles in FoV and guarantee the spatial and temporal smoothness. Experimental results demonstrate the effectiveness and advantage of the proposed method compared with the state-of-the-art methods. That is, our method preserves both the quality and the smoothness of tiles in FoV, thus providing the best QoE for users.Index Terms-360-degree video, field of view, rate adaptation, DASH, video compression, quality of experience.

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“…In this study, we present a strategy that reflects the quality of color, saturation, and contrast, adding brightness correction modes, and we produce high-quality results for rendering simultaneous output panoramas using multi-band.The concept of image stitching is primarily used to prospect the panorama scenes from multiple images of single images. Image stitching method can be grouped into two general ways: [4][5] i. direct techniques and ii. feature based techniques.…”

Section: Introductionmentioning

confidence: 99%

Panoramic Image Stitching with Efficient Brightness Fusion Using RANSAC Algorithm

Shin¹,

Rahim²,

Yun³

2018

IJET

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Background/Objectives: Image stitching can enhance the picture very pleasant by modifying and mixing the different aspects.Therefore, we present panoramic image stitching with efficient brightness fusion which is challenging in different bright sequences taken from different angles.Methods/Statistical analysis:For the problem of brightness, the input image is mixed with sequential images in different brightness.In this works, we proposed atechnique that blends multiple brightness using simple quality measures like color, saturation and contrast.The resulting image quality is good, and most important thing is, the method is efficient since it is simple. Thanthe resulting fused images is applied for panorama image stitching. We used multiband blending to prevent the blurring, and BRIEF (binary robust independent elementary features) method for feature descriptors. We solved the multi-image matching problem using Hamming Distanceusing the binary string based descriptors which is most similar features compare with the second most similar images.We proposed FLANN based matcher to get the more accurate results for using large datasets. We estimate Homography with the matching images using RANSAC algorithms.Findings:An effective structure is performed when we are able to resolve the brightness correction in expose too much or expose for too short a time and the appearance ghost. To solve the unification of brightness, we have collected the input images in different exposures, and selection of the good parts of each picture to an input image for stitching.We removed the blurring from input images, and solved multi-image matching using Hamming distance method. We found better results comparing other methods. For large dataset, we used FLANN based matcher, and estimated the Homography using RANSAC algorithm.Improvements/Applications:We have shown the performance of panoramic image stitching with efficient brightness fusion. We performed stitching with high regulationimages. Finally, we were able to create a panoramic image with efficient brightness fusion.

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Image Stitching based on Feature Extraction Techniques: A Survey

Cited by 70 publications

References 33 publications

An Advanced Benchmarking for Image Compositing Evaluation

An Advanced Benchmarking for Image Compositing Evaluation

Spatial and Temporal Consistency-Aware Dynamic Adaptive Streaming for 360-Degree Videos

Panoramic Image Stitching with Efficient Brightness Fusion Using RANSAC Algorithm

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