  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.
