The union combining the stitching of normal images and unsupervised semantic segmentation of the stitched image is an important region, which is crucial for autonomous driving, intelligent robots, and vehicle detection. This paper designs an union method combining the stitching of normal images and unsupervised semantic segmentation of the stitched image. The normal images are stitched by using the image stitching method designed by Ribeiro D.. The semantic segmentation method for the stitched image uses the method opened in the github. The stitched image contains image distortion. The distortion of the stitched image will make the feature extraction unreasonable. The distortion form of the stitched image is different from the distortion form of the panoramic image combined by line images. Therefore, the DCM proposed by Xing Hu is useless to extract features of the stitched image reasonable. This paper improves the DCM as the improved distortion convolution module (IDCM) by using the deformable convolution, the clamp module, the type transformation module, and the gather module. The IDCM is added before the unsupervised semantic segmentation method opened in the github to extract features reasonable. The IDCM-NUSSM method and the ISM-IDCM-NUSSM method are proposed. The experimental results show the better performance of the designed methods.
| Published in | Machine Learning Research (Volume 9, Issue 2) |
| DOI | 10.11648/j.mlr.20240902.16 |
| Page(s) | 75-79 |
| Creative Commons |
This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited. |
| Copyright |
Copyright © The Author(s), 2024. Published by Science Publishing Group |
Panoramic Image, Image Stitching, Unsupervised Semantic Segmentation
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APA Style
Hu, X., Li, X., Wang, Z., Ren, J., An, Y., et al. (2024). An Union Method Combining the Stitching of Normal Images and the Unsupervised Semantic Segmentation of Stitched Image. Machine Learning Research, 9(2), 75-79. https://doi.org/10.11648/j.mlr.20240902.16
ACS Style
Hu, X.; Li, X.; Wang, Z.; Ren, J.; An, Y., et al. An Union Method Combining the Stitching of Normal Images and the Unsupervised Semantic Segmentation of Stitched Image. Mach. Learn. Res. 2024, 9(2), 75-79. doi: 10.11648/j.mlr.20240902.16
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Download@article{10.11648/j.mlr.20240902.16,
author = {Xing Hu and Xinjian Li and Zhengguang Wang and Jie Ren and Yi An and Cheng Shao and Hongsheng Tian and Qingru Guo},
title = {An Union Method Combining the Stitching of Normal Images and the Unsupervised Semantic Segmentation of Stitched Image
},
journal = {Machine Learning Research},
volume = {9},
number = {2},
pages = {75-79},
doi = {10.11648/j.mlr.20240902.16},
url = {https://doi.org/10.11648/j.mlr.20240902.16},
eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.mlr.20240902.16},
abstract = {The union combining the stitching of normal images and unsupervised semantic segmentation of the stitched image is an important region, which is crucial for autonomous driving, intelligent robots, and vehicle detection. This paper designs an union method combining the stitching of normal images and unsupervised semantic segmentation of the stitched image. The normal images are stitched by using the image stitching method designed by Ribeiro D.. The semantic segmentation method for the stitched image uses the method opened in the github. The stitched image contains image distortion. The distortion of the stitched image will make the feature extraction unreasonable. The distortion form of the stitched image is different from the distortion form of the panoramic image combined by line images. Therefore, the DCM proposed by Xing Hu is useless to extract features of the stitched image reasonable. This paper improves the DCM as the improved distortion convolution module (IDCM) by using the deformable convolution, the clamp module, the type transformation module, and the gather module. The IDCM is added before the unsupervised semantic segmentation method opened in the github to extract features reasonable. The IDCM-NUSSM method and the ISM-IDCM-NUSSM method are proposed. The experimental results show the better performance of the designed methods.
},
year = {2024}
}
TY - JOUR T1 - An Union Method Combining the Stitching of Normal Images and the Unsupervised Semantic Segmentation of Stitched Image AU - Xing Hu AU - Xinjian Li AU - Zhengguang Wang AU - Jie Ren AU - Yi An AU - Cheng Shao AU - Hongsheng Tian AU - Qingru Guo Y1 - 2024/11/29 PY - 2024 N1 - https://doi.org/10.11648/j.mlr.20240902.16 DO - 10.11648/j.mlr.20240902.16 T2 - Machine Learning Research JF - Machine Learning Research JO - Machine Learning Research SP - 75 EP - 79 PB - Science Publishing Group SN - 2637-5680 UR - https://doi.org/10.11648/j.mlr.20240902.16 AB - The union combining the stitching of normal images and unsupervised semantic segmentation of the stitched image is an important region, which is crucial for autonomous driving, intelligent robots, and vehicle detection. This paper designs an union method combining the stitching of normal images and unsupervised semantic segmentation of the stitched image. The normal images are stitched by using the image stitching method designed by Ribeiro D.. The semantic segmentation method for the stitched image uses the method opened in the github. The stitched image contains image distortion. The distortion of the stitched image will make the feature extraction unreasonable. The distortion form of the stitched image is different from the distortion form of the panoramic image combined by line images. Therefore, the DCM proposed by Xing Hu is useless to extract features of the stitched image reasonable. This paper improves the DCM as the improved distortion convolution module (IDCM) by using the deformable convolution, the clamp module, the type transformation module, and the gather module. The IDCM is added before the unsupervised semantic segmentation method opened in the github to extract features reasonable. The IDCM-NUSSM method and the ISM-IDCM-NUSSM method are proposed. The experimental results show the better performance of the designed methods. VL - 9 IS - 2 ER -
School of Control Science and Engineering, Dalian University of Technology, Dalian, China;College of Information Technology, Nanchang Vocational University, Nanchang, China
College of Information Technology, Nanchang Vocational University, Nanchang, China
College of Information Technology, Nanchang Vocational University, Nanchang, China
College of Information Technology, Nanchang Vocational University, Nanchang, China
School of Control Science and Engineering, Dalian University of Technology, Dalian, China;School of Electrical Engineering, Xinjiang University, Urumqi, China
School of Control Science and Engineering, Dalian University of Technology, Dalian, China
State Grid Tieling Electric Power Supply Company, Liaoning, China
Product Development Department, Googosoft, Jinan, China
