A biologically-inspired framework for contour detection using superpixel-based candidates and hierarchical visual cues.
Contour detection has been extensively investigated as a fundamental problem in computer vision. In this study, a biologically-inspired candidate weighting framework is proposed for the challenging task of detecting meaningful contours. In contrast to previous models that detect contours from pixels, a modified superpixel generation processing is proposed to generate a contour candidate set and then weigh the candidates by extracting hierarchical visual cues. We extract the low-level visual local cues to weigh the contour intrinsic property and mid-level visual cues on the basis of Gestalt principles for weighting the contour grouping constraint. Experimental results tested on the BSDS benchmark show that the proposed framework exhibits promising performances to capture meaningful contours in complex scenes.
School of Automation, Huazhong University of Science & Technology, 1037 Luoyu Road, Wuhan 430074, China. sxiao@hust.edu.cn.
Xiao
X
Sun
School of Automation, Huazhong University of Science & Technology, 1037 Luoyu Road, Wuhan 430074, China. shangke1988@hust.edu.cn.
Ke
K
Shang
School of Automation, Huazhong University of Science & Technology, 1037 Luoyu Road, Wuhan 430074, China. mingdelie@yahoo.com.
Delie
D
Ming
School of Automation, Huazhong University of Science & Technology, 1037 Luoyu Road, Wuhan 430074, China. jwtian@hust.edu.cn.
Jinwen
J
Tian
Electronic Information School, Wuhan University, 299 Bayi Road, Wuhan 430072, China. jyma2010@gmail.com.
Jiayi
J
Ma
D000465:Algorithms; D016000:Cluster Analysis; D016208:Databases, Factual; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D008960:Models, Psychological
Gestalt principles; biologically inspired; candidate set; contour detection; hierarchical visual cues