Fast and Efficient Method for Large-Scale Aerial Image Stitching

Pham, Nam; Park, Sihyun; Park, Chun-Su

doi:10.1109/access.2021.3111203

Cited by 16 publications

(7 citation statements)

References 36 publications

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“…Pham et al [18] introduced a fast-adaptive stitching algorithm for large-scale aerial image stitching. Feature extraction and feature matching method is used in the stitching process that finds out the important features of an image.…”

Section: Related Workmentioning

confidence: 99%

Image Stitching Technology in 3D Film Production Based on Digital Image Processing

Peng¹,

Yu²

2022

International Transactions on Electrical Energy Systems

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Image stitching in 3D film production synchronizes multiple independent frame images as a single high-resolution output. The stitching process is common for displaying a large coverage or shot in a single frame containing distinct images. For improving the realistic accuracy of 3D film images, this manuscript introduces an Itinerant Pixel-Matching-based Stitching Process (IPMSP). The proposed stitching process relies on cross-sectional pixels identified in merging two or more images. Based on the linear cumulative distribution in augmenting images, the homogeneity feature is identified. If the homogeneity is high then the stitching for linear pixels occurs increasing the frame resolution and size. The cumulative distribution is determined using the recurrent neural network in verifying homogeneity and contrast. For the contrast pixels identified, the cross-sectional matching is performed for substituting similar pixels in the missing sections. The process is repeated until the stitching region is the same based on the homogeneity feature and increased dimensions. Therefore, this process is capable of improving accuracy, precision, substitution, and reducing errors and complexity.

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Section: Related Workmentioning

confidence: 99%

Image Stitching Technology in 3D Film Production Based on Digital Image Processing

Peng¹,

Yu²

2022

International Transactions on Electrical Energy Systems

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“…Presently, the framework of remote sensing image-stitching algorithms has been perfected. It is the primary research focus on studying the fast-stitching algorithm of remote sensing images and its adaptability and robustness to different scenes [19]- [21]. The panoramic stitching method of remote sensing images of straw farmlands is studied through low-altitude and high-resolution sequence images collected by the UAV, as shown in Figure 1.…”

Section: Proposed Methods a Fast Stitching Algorithmmentioning

confidence: 99%

Farmland Aerial Images Fast-Stitching Method and Application Based on Improved SIFT Algorithm

Liu

Wang

et al. 2022

IEEE Access

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The main black land conservation measure in China is the straw return to the fields. The processing of high-resolution images collected by aerial photography of UAVs through image stitching technology can provide image information for achieving fast and accurate detection of straw cover over large areas. The classical SIFT algorithm has many drawbacks, such as high dimensionality of feature descriptors, high computational effort, and low matching efficiency. To solve the problems above, this study proposes an improved algorithm. First, the method down sampled the high-resolution images before detecting the features to reduce the number of feature points and improve the efficiency of feature detection. Then, matching among feature points is achieved by gradient normalization-based feature descriptors to improve the matching accuracy. Next, the Progressive Sample Consistency algorithm eliminates the mismatch points and optimizes the transformation model. Finally, the images are fused with optimal stitching combined with fade-in and fade-out to achieve high-quality stitching. The comparative experimental results show that compared with the traditional SIFT and the speed-up robust feature algorithms, the algorithm has the advantage of the speed and good robustness to angle rotation, and makes full use of the texture information and the detail information, so it has higher accuracy. Compared with the traditional methods, the panoramic stitching image quality herein is excellent and can be applied to subsequent straw cover detection, the straw cover error is ≤3%, meeting the demand for large-area straw cover detection. Overall, the method proposed herein achieves an ideal balance between accuracy and efficiency; and outperforms other widely used and superior methods.INDEX TERMS Aerial images, down sampling, SIFT operator, panoramic stitching, straw coverage rate.

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“…The OTSU (Pham et al, 2021) algorithm can obtain the value of T corresponding to the maximum inter-class variance of the frame, which is the best threshold value to distinguish the foreground from the background. For the input vehicle image I x y , ( ) , the proportion of foreground pixel points less than the threshold value T to the whole image pixel points is w 0 , and the average gray level is m 0 .…”

Section: Otsu Algorithm Was Used To Obtain the Value Of Tmentioning

confidence: 99%

PRESCAN Adaptive Vehicle Image Real-Time Stitching Algorithm Based on Improved SIFT

Ding

2023

International Journal of Information Technologies and Systems Approach

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Aiming at the problems of high latency, low accuracy, and obvious stitching gaps of traditional image stitching techniques in PRESCAN, this paper proposes an adaptive real-time vehicle image stitching algorithm based on improved scale-invariant feature transform (SIFT). Firstly, a multi-threaded structure is utilized to combine the improved adaptive features from accelerated segment test algorithm with SIFT descriptors for feature extraction. Secondly, a mismatching filtering strategy based on the BF algorithm has been introduced. Finally, image stitching is performed by combining random sample consensus algorithm and adaptive region stitching strategy. The results show that the recall rate is about 30.64%, the accuracy of matching is about 98.86%, and the total stitching time is about 22.82ms. It effectively improves the quality of feature point extraction and matching accuracy and significantly reduces the stitching time of images. It provides a real-time and accurate vehicle image stitching method for the field of perception in the autonomous driving laboratory environment.

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Fast and Efficient Method for Large-Scale Aerial Image Stitching

Cited by 16 publications

References 36 publications

Image Stitching Technology in 3D Film Production Based on Digital Image Processing

Image Stitching Technology in 3D Film Production Based on Digital Image Processing

Farmland Aerial Images Fast-Stitching Method and Application Based on Improved SIFT Algorithm

PRESCAN Adaptive Vehicle Image Real-Time Stitching Algorithm Based on Improved SIFT

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