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.

中文翻译：

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使用无偏稀疏约束色对手响应和超像素对比度进行边界检测

边界在各种基于图像的任务中起着至关重要的作用，但是许多现有的基于非学习的边界检测方法在识别复杂背景下的真实边界方面表现不佳。在这项研究中，作者使用稀疏约束的颜色对手响应和超像素对比度解决了这个问题。首先，基于生物学启发的色彩对手机制，作者阐述了一种计算无偏稀疏约束的色彩对手响应的方法。在此过程中，显示​​颜色变化的位置得到了增强，而纹理位置则通过局部稀疏性度量的提示而被初步抑制。其次，借助于超像素分割，作者提出了一种获得超像素对比度图的有效方法。这种方法有助于在抑制纹理时利用对象形状信息。因此，作者提出了一种基于非学习的方法来检测图像中的边界，将无偏的稀疏约束的颜色对手响应与整个超像素对比度图相结合。在广泛采用的数据集上的实验结果表明，作者方法优于大多数竞争方法。特别地，与最新的环绕声调制方法相比，所提出的方法获得了可比的性能，同时消耗更少的运行时间。在广泛采用的数据集上的实验结果表明，作者方法优于大多数竞争方法。特别地，与最新的环绕声调制方法相比，所提出的方法在消耗更少的运行时间的同时获得了可比的性能。在广泛采用的数据集上的实验结果表明，作者方法优于大多数竞争方法。特别地，与最新的环绕声调制方法相比，所提出的方法获得了可比的性能，同时消耗更少的运行时间。