CT-Bound: Robust Boundary Detection From Noisy Images Via Hybrid Convolution and Transformer Neural Networks
<|reference_start|>CT-Bound: Robust Boundary Detection From Noisy Images Via Hybrid Convolution and Transformer Neural Networks: We present CT-Bound, a robust and fast boundary detection method for very noisy images using a hybrid Convolution and Transformer neural network. The proposed architecture decomposes boundary estimation into two tasks: local detection and global regularization. During the local detection, the model uses a convolutional architecture to predict the boundary structure of each image patch in the form of a pre-defined local boundary representation, the field-of-junctions (FoJ). Then, it uses a feed-forward transformer architecture to globally refine the boundary structures of each patch to generate an edge map and a smoothed color map simultaneously. Our quantitative analysis shows that CT-Bound outperforms the previous best algorithms in edge detection on very noisy images. It also increases the edge detection accuracy of FoJ-based methods while having a 3-time speed improvement. Finally, we demonstrate that CT-Bound can produce boundary and color maps on real captured images without extra fine-tuning and real-time boundary map and color map videos at ten frames per second.<|reference_end|>
@article{xu2024ct-bound:,
    title={CT-Bound: Robust Boundary Detection From Noisy Images Via Hybrid
  Convolution and Transformer Neural Networks},
    author={Wei Xu, Junjie Luo, Qi Guo},
    journal={arXiv preprint arXiv:2403.16494},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.16494},
    primaryClass={cs.CV}
}