Remote consultation image stitching method based on wireless sensor technology and mathematical morphology

Authors

  • Xiaoge Li Department of Culture and Education, Pingdingshan Polytechnic College, Pingdingshan 467000, China

DOI:

https://doi.org/10.4108/eetpht.v8i31.700

Keywords:

wireless sensor technology, mathematical morphology, image stitching, image acquisition, image denoising, image registration

Abstract

INTRODUCTION: In order to obtain seamless and high-precision remote consultation image mosaic results, a remote consultation image mosaic method based on wireless sensor technology and mathematical morphology is studied. A consultation image acquisition unit based on wireless sensor technology is designed, and the remote consultation image signal is collected by sensor; In the process of signal conditioning, a filter based on mathematical morphology is used to reduce the influence of noise on the accuracy of remote consultation image acquisition.

OBJECTIVES: Compressed sensing technology is used to realize the compression, transmission and recovery of consultation image sampling data.

METHODS: After preprocessing the image through shadow correction, surf algorithm is used to construct the scale space to determine the main direction of feature points in the image; The extended surf descriptor is constructed based on feature points for consultation image registration.

RESULTS: Based on the spatial transformation relationship between images, the improved gradual in and gradual out stitching method is used to complete the remote consultation image stitching. Experimental results show that this method can accurately collect consultation image signals, and the corresponding rate of the feature point extraction results reaches nearly 99%, which is relatively robust.

CONCLUSION: The RMSE error of the image registration results is less than 2.692, which improves the accuracy of the remote consultation image stitching results, well solves the problem of image visual field reduction, and there is no seam in the stitching area.

Downloads

Download data is not yet available.

References

Menter, T., Nicolet, S., Baumhoer, D., Tolnay, M. & Tzankov, A. (2020). Intraoperative frozen section consultation by remote whole-slide imaging analysis –validation and comparison to robotic remote microscopy. Journal of Clinical Pathology, 73(6), 206-261. DOI: https://doi.org/10.1136/jclinpath-2019-206261

Scheidt, S., Ramsey, M. & Lancaster, N. (2020). Radiometric normalization and image stitching generation of aster thermal infrared data: an application to extensive sand sheets and dune fields. Remote Sensing of Environment, 112(3), 920-933. DOI: https://doi.org/10.1016/j.rse.2007.06.020

Shuai, L., Shuai, W., Xinyu, L., Amir, H. G., Mahmoud, D., Khan, M. & Victor, H. C. De A. (2021) Human Memory Update Strategy: A Multi-Layer Template Update Mechanism for Remote Visual Monitoring, IEEE Transactions on Multimedia, 23, 2188-2198

Liu, S., Wang, S., Liu, X., Dai, J., Khan, M., Gandomi, A. H., Ding, W. & de Albuquerque, V. H. C. (2022) Human Inertial Thinking Strategy: A Novel Fuzzy Reasoning Mechanism for IoT-Assisted Visual Monitoring, IEEE Internet of Things Journal, online first, doi: 10.1109/JIOT.2022.3142115 DOI: https://doi.org/10.1109/JIOT.2022.3142115

Liu, J. & Bu, F. L. (2019). Improved RANSAC features image-matching method based on surf. The Journal of Engineering, 2019(5), 20-33. DOI: https://doi.org/10.1049/joe.2018.9198

Shi, Z., Li, H., Cao, Q., Ren, H. & Fan, B. (2020). An image stitching method based on convolutional neural network semantic features extraction. Journal of Signal Processing Systems, 92(2), 2-3.

Tian, J., Wu, Y., Cai, Y., Fan, H. & Yu, W. (2021). A novel mosaic method for spaceborne scansar images based on homography matrix compensation. Remote Sensing, 13(15), 2866-2871. DOI: https://doi.org/10.3390/rs13152866

Chaitra R., Rajaram M G. (2020). Development of image stitching using feature detection and feature matching techniques. IEEE International Conference for Innovation in Technology, 11(1), 188-190.

Ramin Z. (2020). Object-oriented image stitching. International Symposium on Visual Computing, 11(2), 240-246.

Yuya N., Ryosuke H., Masahiro I. (2020). Naturalness-preserving image stitching based on optimal seam estimation considering parallax. Global Conference on Life Sciences and Technologies, 6(33), 147-149

Mostafa R., Ahmad M., Mohammad F., Jamal C. (2020). Real-time SLAM based on image stitching for autonomous navigation of UAVs in GNSS-Denied regions. IEEE International Conference on Artificial Intelligence Circuits and Systems, 17(2), 145-148.

Moussaoui, H., Nakajo, A., Rinaldi, G., Hubert, M., Laurencin, J. & Herle, J. V. (2021). Modeling nickel microstructural evolution in NI-YSZ electrodes using a mathematical morphology approach. ECS Transactions, 103(1), 997-1009. DOI: https://doi.org/10.1149/10301.0997ecst

Van-D. H., Diem-Phuc T., Nguyen G. N., Anh P., Van-Huy P. (2020). Deep feature extraction for panoramic image stitching. Asian Conference on Intelligent Information and Database Systems, 100(8), 914-919

Liu, S., He, T., Li, J., Li, Y. & Kumar, A. (2021) An Effective Learning Evaluation Method Based on Text Data with Real-time Attribution - A Case Study for Mathematical Class with Students of Junior Middle School in China, ACM Transactions on Asian and Low-Resource Language Information Processing, online published, doi:10.1145/3474367 DOI: https://doi.org/10.1145/3474367

Parham N., Shakcb D., Fernando L., Clemente I-C., Nicolas P. Avdelidis X. M. (2020). Reflectivity detection and reduction of thermographic images using image stitching technique and its applications on remote inspection. Conference on Thermosense: Thermal Infrared Applications, 45(5),762-766.

Kyu-Yul L., Jae-Young S.(2020). Warping residual based image stitching for large parallax. IEEE/CVF Conference on Computer Vision and Pattern Recognition, 66(9), 129-135.

Gonzalo R., Marta V., Jaime S., Gemma U., Luisa R., Manuel V., Alberto M., Eduardo J., Luis J., Miguel C., Alfonso L., César S. (2020). Hyperspectral images acquisition: an efficient capture and processing stitching procedure for medical environments. Onference on Design of Circuits and Integrated Systems, 77(19), 781-790

Mukhammadali K., Jong-Ki H. (2020). Efficient stitching algorithm for stereoscopic VR Images. IEEE International Conference on Consumer Electronics - Asia, 44(18), 716-720

Riza R. A., Pineda, K E., Peñas D. P. M. (2020). Automated stitching of coral reef images and extraction of features for damselfish shoaling behavior analysis. IEEE Region 10 Conference, 67(7), 239-245

Anthony S., Matthew M., Charles R., Justin L., Daniel D., Peter W, E. S. (2020). Ice topography reconstruction and panoramic stitching using forward looking sonar images. Conference on Global Oceans : Singapore – U.S. Gulf Coast, 65(13), 255-261.uthor AA, Author BB, Author CC, Author DD. Title of article. Abbreviated title of journal. Year of publication; volume number(issue number):page numbers.

Downloads

Published

27-07-2022

How to Cite

1.
Li X. Remote consultation image stitching method based on wireless sensor technology and mathematical morphology. EAI Endorsed Trans Perv Health Tech [Internet]. 2022 Jul. 27 [cited 2024 Nov. 23];8(31):e5. Available from: https://publications.eai.eu/index.php/phat/article/view/700