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Interactive Image Segmentation Technique Based on Improved Residual Network | IEEE Journals & Magazine | IEEE Xplore

Interactive Image Segmentation Technique Based on Improved Residual Network


This study established an interactive center point extreme detection method for image segmentation. By combining the Canny algorithm and edge image detection analysis, im...

Abstract:

Interactive image segmentation offers useful guidance to users and can be applied in practical settings for production and daily life purposes. Nonetheless, the technolog...Show More

Abstract:

Interactive image segmentation offers useful guidance to users and can be applied in practical settings for production and daily life purposes. Nonetheless, the technology’s intrinsic limitations, including complicated interaction methods and high error rates, have impeded its further advancement with the development of computer vision. To address the issue, this study introduces a method to determine the extreme point of center point prediction. The target center serves as the “symmetric center” of the extreme point, which facilitates the search for other extreme points. Furthermore, the Canny algorithm is combined to achieve edge image detection. Moreover, the residual network is enhanced through embedding a pre-activation step, introducing a BatchNorm layer, and adding a pyramid scene parsing network. Finally, the performance of this method is verified by analyzing its Intersection over Union, segmentation accuracy, efficiency, F1 value, and other indicators. The results show that on the Pascalvoc2012 dataset, the segmentation accuracy obtained through the extreme point method can reach over 90%. The addition of the pyramid scene analysis network stabilizes its accuracy on urban landscape datasets between 92% and 96%. When the proposed image segmentation method is applied to the Grabcut dataset, its union intersection can reach 88.7%. On the self-generated complex daily scenery dataset, this method achieves segmentation accuracy of 95% with superior stability and precision. This provides a fresh methodological reference for optimizing interactive image segmentation technology further.
This study established an interactive center point extreme detection method for image segmentation. By combining the Canny algorithm and edge image detection analysis, im...
Published in: IEEE Access ( Volume: 11)
Page(s): 131597 - 131609
Date of Publication: 23 November 2023
Electronic ISSN: 2169-3536

Funding Agency:

Author image of Feng Yang
Computer Center, Anshan Normal University, Anshan, China
Feng Yang was born in Liaoning, China, in 1976. He received the bachelor’s degree in computer software from Liaoning University, China, in 1999, and the M.Eng. degree in computer software from the Dalian University of Technology, in 2008. Since 1999, he has been a Lecturer with Anshan Normal University, Liaoning. His professional title is Associate Professor. He has authored four books and over ten articles. His current r...Show More
Feng Yang was born in Liaoning, China, in 1976. He received the bachelor’s degree in computer software from Liaoning University, China, in 1999, and the M.Eng. degree in computer software from the Dalian University of Technology, in 2008. Since 1999, he has been a Lecturer with Anshan Normal University, Liaoning. His professional title is Associate Professor. He has authored four books and over ten articles. His current r...View more
Author image of Dan Geng
Science and Technology Division, Anshan Normal University, Anshan, China
Dan Geng was born in Liaoning, China, in 1977. She received the bachelor’s degree in computer science from Northeast University, China, in 1999, and the M.Eng. degree in computer software from the Dalian University of Technology, in 2008. Since 1999, she has been with Anshan Normal University, Liaoning. Her professional title is Associate Researcher and the Deputy Director of the Big Data Research Institute, Anshan Normal...Show More
Dan Geng was born in Liaoning, China, in 1977. She received the bachelor’s degree in computer science from Northeast University, China, in 1999, and the M.Eng. degree in computer software from the Dalian University of Technology, in 2008. Since 1999, she has been with Anshan Normal University, Liaoning. Her professional title is Associate Researcher and the Deputy Director of the Big Data Research Institute, Anshan Normal...View more

Author image of Feng Yang
Computer Center, Anshan Normal University, Anshan, China
Feng Yang was born in Liaoning, China, in 1976. He received the bachelor’s degree in computer software from Liaoning University, China, in 1999, and the M.Eng. degree in computer software from the Dalian University of Technology, in 2008. Since 1999, he has been a Lecturer with Anshan Normal University, Liaoning. His professional title is Associate Professor. He has authored four books and over ten articles. His current research interests include image recognition, bbreak cloud computing, and wireless sensor networks.
Feng Yang was born in Liaoning, China, in 1976. He received the bachelor’s degree in computer software from Liaoning University, China, in 1999, and the M.Eng. degree in computer software from the Dalian University of Technology, in 2008. Since 1999, he has been a Lecturer with Anshan Normal University, Liaoning. His professional title is Associate Professor. He has authored four books and over ten articles. His current research interests include image recognition, bbreak cloud computing, and wireless sensor networks.View more
Author image of Dan Geng
Science and Technology Division, Anshan Normal University, Anshan, China
Dan Geng was born in Liaoning, China, in 1977. She received the bachelor’s degree in computer science from Northeast University, China, in 1999, and the M.Eng. degree in computer software from the Dalian University of Technology, in 2008. Since 1999, she has been with Anshan Normal University, Liaoning. Her professional title is Associate Researcher and the Deputy Director of the Big Data Research Institute, Anshan Normal University. Her current research interests include image recognition, cloud computing, and wireless sensor networks.
Dan Geng was born in Liaoning, China, in 1977. She received the bachelor’s degree in computer science from Northeast University, China, in 1999, and the M.Eng. degree in computer software from the Dalian University of Technology, in 2008. Since 1999, she has been with Anshan Normal University, Liaoning. Her professional title is Associate Researcher and the Deputy Director of the Big Data Research Institute, Anshan Normal University. Her current research interests include image recognition, cloud computing, and wireless sensor networks.View more

References

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