Contour detection based on anisotropic edge strength and hierarchical superpixel contrast

Abstract

Contour detection is a fundamental problem in computer vision, yet existing methods usually suffer from the interference of noise and textures. To address this problem, we present an unsupervised contour detection method based on anisotropic edge strength and hierarchical superpixel contrast. The anisotropic edge strength is obtained through the first derivative of anisotropic Gaussian kernels which incorporates an adaptive anisotropy factor. The anisotropic kernel improves the robustness to noise, while the adaptive anisotropic factor attenuates the anisotropy stretch effect. Using a method based on region merging, we obtain a hierarchical set of superpixel maps and thus compute superpixel contrast maps at different hierarchy levels. Consequently, the contour strength map is obtained by multiplying the anisotropic edge strength map by the average of the hierarchical superpixel contrast maps. Experimental results on two publicly available datasets validate the superiority of the proposed method over the competing methods. On the Berkeley Segmentation Dataset & Benchmark 300 and the Berkeley Segmentation Dataset & Benchmark 500, our method obtains (optimal dataset scale) F-measure values of 0.63 and 0.67, respectively, an improvement of at least 0.06 over the competing methods.