Underwater Terrain Image Stitching Based on Spatial Gradient Feature Block

Wang, Zhenzhou; Li, Jiashuo; Wang, Xiang; Niu, Xuanhao

doi:10.32604/cmc.2022.027017

Cited by 4 publications

(3 citation statements)

References 30 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The MSAC (M-estimate sample consensus) algorithm is an improved algorithm based on RANSAC (random sample consensus) [ 28 , 29 ]. The specific implementation steps are as follows:…”

Section: Methodsmentioning

confidence: 99%

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

Deng

Song²,

Han³

et al. 2023

Sensors

View full text Add to dashboard Cite

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.

show abstract

“…The MSAC (M-estimate sample consensus) algorithm is an improved algorithm based on RANSAC (random sample consensus) [ 28 , 29 ]. The specific implementation steps are as follows:…”

Section: Methodsmentioning

confidence: 99%

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

Deng

Song²,

Han³

et al. 2023

Sensors

View full text Add to dashboard Cite

show abstract

“…Finally, in the actual seabed acoustic image application, the fused data matching accuracy exceeds 70% [116]. To accurately realize the tracking effect of seafloor pipeline sutures, Wang et al combined spatial information and gradient information, and used the KAZE algorithm to segment the image into multiple regions [117]. It used the global optimal stitching method to realize the fusion of seabed pipeline targets, improving image superposition accuracy.…”

Section: Other Technical Applicationsmentioning

confidence: 99%

Sonar image intelligent processing in seabed pipeline detection: review and application

Shi,

Cao,

et al. 2024

Meas. Sci. Technol.

View full text Add to dashboard Cite

Subsea pipelines rely primarily on imaging sonar for detection and identification. We analyze the imaging principles of side scan sonar, multi-beam sonar, synthetic aperture sonar, seafloor penetrating sonar and forward-looking sonar. We discuss their effectiveness in detecting seabed pipelines, as well as their limitations in image recognition capabilities. As intelligent algorithms have become increasingly important in the field of image processing, we review the sonar image intelligent detection and recognition algorithms in the past six years and summarize the internal principles and application effects of classic algorithms such as SIFT (Scale-Invariant Feature Transform), KMA (K-means algorithm), and CFAR (Constant False-Alarm Rate) that currently show good application prospects. Simultaneously, we review the particular strengths exhibited by these algorithms, such as contour feature extraction, image segmentation and clustering, target recognition under background noise, etc. The research on intelligent processing of sonar images opens up a new way to solve the difficult problem of the seabed targets detection and recognition.

show abstract

“…The traditional multi-image stitching method entails finding the projection models of all images in the stitching sequence and using a combination of multiple models to achieve panoramic stitching of multiple images [24], [25]. This method is simple to operate, but causes stitching errors to accumulate.…”

Section: E Multiple High-resolution-images Panoramic Stitchingmentioning

confidence: 99%

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

Liu

Wang

et al. 2022

IEEE Access

View full text Add to dashboard Cite

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.

show abstract

Underwater Terrain Image Stitching Based on Spatial Gradient Feature Block

Cited by 4 publications

References 30 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

Sonar image intelligent processing in seabed pipeline detection: review and application

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

Contact Info

Product

Resources

About