Junchi Yan, Jun Wang, et al.
IEEE TIP
Contour detection is an important and fundamental problem in computer vision which finds numerous applications. Despite significant progress has been made in the past decades, contour detection from natural images remains a challenging task due to the difficulty of clearly distinguishing between edges of objects and surrounding backgrounds. To address this problem, we first capture multi-scale features from pixel-level to segment-level using local and global information. These features are mapped to a space where discriminative information is captured by computing posterior divergence of Gaussian mixture models and sufficient statistics based on deep Boltzmann machine. Then we introduce a stacking random forest learning framework for contour detection. We evaluate the proposed algorithm against leading methods in the literature on the Berkeley segmentation and Weizmann horse data sets. Experimental results demonstrate that the proposed contour detection algorithm performs favorably against state-of-the-art methods in terms of speed and accuracy.
Junchi Yan, Jun Wang, et al.
IEEE TIP
Tianshu Yu, Junchi Yan, et al.
CVPR 2018
Ning Zhang, Junchi Yan, et al.
IJCAI 2018
Xiaoxing Wang, Chao Xue, et al.
IJCAI 2020