Interactive segmentation based on deformable convolution and context feature calibration

Shanshan Song

doi:10.1117/12.3032816

5 July 2024 Interactive segmentation based on deformable convolution and context feature calibration

Shanshan Song

Proceedings Volume 13184, Third International Conference on Electronic Information Engineering and Data Processing (EIEDP 2024); 131841R (2024) https://doi.org/10.1117/12.3032816
Event: 3rd International Conference on Electronic Information Engineering and Data Processing (EIEDP 2024), 2024, Kuala Lumpur, Malaysia

Abstract

The advantage of interactive segmentation is that it can be adjusted in real-time according to user requests and feedback, leading to more precise and expected segmentation results. This research provides a new interactive segmentation method to increase the accuracy of image segmentation. The proposed method uses deformable convolutions instead of traditional convolutions to better capture spatial variations in the target area. In addition, the Context Feature Calibration module is introduced to model various semantic context information for each pixel, thereby further enhancing the accuracy of segmentation results. The proposed method was verified using multiple public datasets. The results show that the method exhibits good performance. In summary, the proposed method offers a novel and effective solution to the interactive segmentation task.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Shanshan Song "Interactive segmentation based on deformable convolution and context feature calibration", Proc. SPIE 13184, Third International Conference on Electronic Information Engineering and Data Processing (EIEDP 2024), 131841R (5 July 2024); https://doi.org/10.1117/12.3032816

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