An improved surround suppression model based on orientation contrast for boundary detection

Hui Zhang, Bojun Xie, Jian Yu

Abstract: This paper proposes an unsupervised bottom-up boundary detection algorithm, which is an improved surround suppression model based on orientation contrast. First, the candidate boundary set is obtained by the edge focusing algorithm. Second, the orientation contrast map is constructed using the response of Gabor filter. The suppression term is computed on orientation contrast map using steerable filter, which can effectively differentiate step edge from texture edge. Using low-level image features, the boundary map can be used as preprocessing step for image segmentation and/or object detection. The detection approach has been validated on Rug dataset and the average of figure of merit shows an improvement of 15%.