Salient traffic sign detection based on multiscale hypercomplex fourier transform
Ce Li, Yaling Hu · 2011
The paper proposes a new approach to detect salient traffic signs, which is based on visual saliency and auto-generated strokes for image segmentation. The proposed algorithm deals with two tasks on detecting traffic signs: auto-location and auto-extraction. Firstly, inspired by recent work of visual saliency detection, we obtain the location of traffic signs in a natural image by multi-scale phase spectrum of quaternion Fourier transformation (MSPQFT). Secondly, in order to extract traffic signs, auto-generated strokes are used instead of drawing the strokes by the users, the sign board area is extracted using automatic interactive image segmentation. Extensive experiments on public datasets show that our approach outperforms state-of-the-art methods remarkably in salient traffic sign detection. Moreover, the proposed detection method has higher accurate rate and robustness to different natural scenes.