UAV Image Stitching Based on Optimal Seam and Half-Projective Warp

Chen, Jun; Li, Zixian; Peng, Chengli; Wang, Yong; Gong, Wenping

doi:10.3390/rs14051068

Cited by 22 publications

(13 citation statements)

References 43 publications

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“…(3) After counting the number of interior points, if the number of interior points corresponding to the current transmission projection matrix is the largest one, the model is considered to be the optimal model [ 32 ].…”

Section: Methodsmentioning

confidence: 99%

Geological Borehole Video Image Stitching Method Based on Local Homography Matrix Offset Optimization

Deng

Song²,

Han³

et al. 2023

Sensors

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Due to the influence of the shooting environment and inherent image characteristics, there is a large amount of interference in the process of image stitching a geological borehole video. To accurately match the acquired image sequences in the inner part of a borehole, this paper presents a new method of stitching an unfolded borehole image, which uses the image generated from the video to construct a large-scale panorama. Firstly, the speeded-up robust feathers (SURF) algorithm is used to extract the image feature points and complete the rough matching. Then, the M-estimator sample consensus (MSAC) algorithm is introduced to remove the mismatched point pairs and obtain the homography matrix. Subsequently, we propose a local homography matrix offset optimization (LHOO) algorithm to obtain the optimal offset. Finally, the above process is cycled frame by frame, and the image sequence is continuously stitched to complete the construction of a cylindrical borehole panorama. The experimental results show that compared with those of the SIFT, Harris, ORB and SURF algorithms, the matching accuracy of our algorithm has been greatly improved. The final test is carried out on 225 consecutive video frames, and the panorama has a good visual effect, and the average time of each frame is 100 ms, which basically meets the requirements of the project.

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Section: Methodsmentioning

confidence: 99%

Geological Borehole Video Image Stitching Method Based on Local Homography Matrix Offset Optimization

Deng

Song²,

Han³

et al. 2023

Sensors

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show abstract

“…Nowadays, feature-matching algorithms have powerful functions and are often used for image stitching [ 29 , 30 , 31 ], positioning, mapping, registration, and other visual tasks. However, using this technology for scenes with sparse textures and tasks requiring a high real-time performance and accuracy remains challenging.…”

Section: Related Workmentioning

confidence: 99%

A Real-Time Registration Algorithm of UAV Aerial Images Based on Feature Matching

Liu

Xiao

et al. 2023

J. Imaging

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This study aimed to achieve the accurate and real-time geographic positioning of UAV aerial image targets. We verified a method of registering UAV camera images on a map (with the geographic location) through feature matching. The UAV is usually in rapid motion and involves changes in the camera head, and the map is high-resolution and has sparse features. These reasons make it difficult for the current feature-matching algorithm to accurately register the two (camera image and map) in real time, meaning that there will be a large number of mismatches. To solve this problem, we used the SuperGlue algorithm, which has a better performance, to match the features. The layer and block strategy, combined with the prior data of the UAV, was introduced to improve the accuracy and speed of feature matching, and the matching information obtained between frames was introduced to solve the problem of uneven registration. Here, we propose the concept of updating map features with UAV image features to enhance the robustness and applicability of UAV aerial image and map registration. After numerous experiments, it was proved that the proposed method is feasible and can adapt to the changes in the camera head, environment, etc. The UAV aerial image is stably and accurately registered on the map, and the frame rate reaches 12 frames per second, which provides a basis for the geo-positioning of UAV aerial image targets.

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“…2 (16) where I H (i, j) is the gray value of pixels located in row i and column j of the spliced image, I W (i, j) is the gray value of pixels located in row i and column j of the original image.…”

Section: Quantitative and Objective Evaluation Of Fusion Effectmentioning

confidence: 99%

“…In 2021, Fan et al [13] proposed a fast adaptive stitching algorithm for processing a large number of aerial images, by reducing the estimated reprojection error and the number of observed visual distortions, culling densely overlapping images from all images, improving the quality of the stitched image. Yuan et al [14] proposed a superpixel-based slitting strategy, Xue et al [15] proposed to identify and eliminate ghosting through multi-component collaboration, and Chen et al [16] proposed a stitching strategy based on optimal seams and half-projection warping in 2022; they all optimize the stitching quality of the images.…”

Section: Introductionmentioning

confidence: 99%

UAV Low-Altitude Aerial Image Stitching Based on Semantic Segmentation and ORB Algorithm for Urban Traffic

et al. 2022

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UAVs are flexible in action, changeable in shooting angles, and complex and changeable in the shooting environment. Most of the existing stitching algorithms are suitable for images collected by UAVs in static environments, but the images are in fact being captured dynamically, especially in low-altitude flights. Considering that the great changes of the object position may cause the low-altitude aerial images to be affected by the moving foreground during stitching, so as to result in quality problems, such as splicing misalignment and tearing, a UAV aerial image stitching algorithm is proposed based on semantic segmentation and ORB. In the image registration, the algorithm introduces a semantic segmentation network to separate the foreground and background of the image and obtains the foreground semantic information. At the same time, it uses the quadtree decomposition idea and the classical ORB algorithm to extract feature points. By comparing the feature point information with the foreground semantic information, the foreground feature points can be deleted to realize feature point matching. Based on the accurate image registration, the image stitching and fusion will be achieved by the homography matrix and the weighted fusion algorithm. The proposed algorithm not only preserves the details of the original image, but also improves the four objective data points of information entropy, average gradient, peak signal-to-noise ratio and root mean square error. It can solve the problem of splicing misalignment tearing during background stitching caused by dynamic foreground and improves the stitching quality of UAV low-altitude aerial images.

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UAV Image Stitching Based on Optimal Seam and Half-Projective Warp

Cited by 22 publications

References 43 publications

Geological Borehole Video Image Stitching Method Based on Local Homography Matrix Offset Optimization

Geological Borehole Video Image Stitching Method Based on Local Homography Matrix Offset Optimization

A Real-Time Registration Algorithm of UAV Aerial Images Based on Feature Matching

UAV Low-Altitude Aerial Image Stitching Based on Semantic Segmentation and ORB Algorithm for Urban Traffic

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