Sofou Anastasia, Maragos Petros
School of Electrical and Computer Engineering, National Technical University of Athens, Athens, Greece.
IEEE Trans Image Process. 2008 Mar;17(3):364-76. doi: 10.1109/TIP.2007.916156.
Image segmentation remains an important, but hard-to-solve, problem since it appears to be application dependent with usually no a priori information available regarding the image structure. Moreover, the increasing demands of image analysis tasks in terms of segmentation results' quality introduce the necessity of employing multiple cues for improving image segmentation results. In this paper, we attempt to incorporate cues such as intensity contrast, region size, and texture in the segmentation procedure and derive improved results compared to using individual cues separately. We emphasize on the overall segmentation procedure, and we propose efficient simplification operators and feature extraction schemes, capable of quantifying important characteristics, like geometrical complexity, rate of change in local contrast variations, and orientation, that eventually favor the final segmentation result. Based on the well-known morphological paradigm of watershed transform segmentation, which exploits intensity contrast and region size criteria, we investigate its partial differential equation (PDE) formulation, and we extend it in order to satisfy various flooding criteria, thus making it applicable to a wider range of images. Going a step further, we introduce a segmentation scheme that couples contrast criteria in flooding with texture information. The modeling of the proposed scheme is done via PDEs and the efficient incorporation of the available contrast and texture information, is done by selecting an appropriate cartoon-texture image decomposition scheme. The proposed coupled segmentation scheme is driven by two separate image components: cartoon U (for contrast information) and texture component V. The performance of the proposed segmentation scheme is demonstrated through a complete set of experimental results and substantiated using quantitative and qualitative criteria.
图像分割仍然是一个重要但难以解决的问题，因为它似乎依赖于应用，而且通常没有关于图像结构的先验信息。此外，图像分析任务对分割结果质量的要求越来越高，这就需要采用多种线索来改善图像分割结果。在本文中，我们尝试在分割过程中纳入强度对比度、区域大小和纹理等线索，并与单独使用单个线索相比得出改进的结果。我们强调整体分割过程，并提出有效的简化算子和特征提取方案，这些方案能够量化重要特征，如几何复杂度、局部对比度变化的变化率和方向，最终有利于最终的分割结果。基于分水岭变换分割这一著名的形态学范式，它利用强度对比度和区域大小标准，我们研究其偏微分方程（PDE）公式，并对其进行扩展以满足各种泛洪标准，从而使其适用于更广泛的图像。更进一步，我们引入一种分割方案，该方案将泛洪中的对比度标准与纹理信息相结合。所提出方案的建模通过偏微分方程完成，并且通过选择合适的卡通纹理图像分解方案来有效地纳入可用的对比度和纹理信息。所提出的耦合分割方案由两个单独的图像分量驱动：卡通分量U（用于对比度信息）和纹理分量V。通过完整的实验结果展示了所提出分割方案的性能，并使用定量和定性标准进行了验证。
