Boundary detection using unbiased sparseness‐constrained colour‐opponent response and superpixel contrast

Boundaries play a crucial role in various image‐based tasks, but many existing non‐learning‐based boundary detection methods underperform in recognising authentic boundaries from a complex background. In this study, the authors address this problem using the sparseness‐constrained colour‐opponent response and the superpixel contrast. First, building on the biologically inspired colour‐opponency mechanism, the authors elaborate a method to compute the unbiased sparseness‐constrained colour‐opponent response. In this procedure, locations showing colour variations are enhanced, while the textural locations are preliminarily suppressed by the cue of local sparseness measure. Second, with the help of superpixel segmentation, the authors present an effective approach to obtain the superpixel contrast map. This approach helps to exploit the object shape information in suppressing textures. Consequently, the authors propose a non‐learning‐based method to detect boundaries in images, combining the unbiased sparseness‐constrained colour‐opponent response and the overall superpixel contrast map. Experiment results on widely adopted datasets manifest that the authors method outperforms most of the competing methods. In particular, compared with the state‐of‐the‐art surround‐modulation method, the proposed method obtains a comparable performance while consuming much less runtime.