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Model-Based Image Interpretation under Uncertainty and Fuzziness

机译：不确定性和模糊性下基于模型的图像解释

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Structural models such as ontologies and graphs can encode generic knowledge about a scene observed in an image. Their use in spatial reasoning schemes allows driving segmentation and recognition of objects and structures in images. The developed methods include finding a best segmentation path in a graph, global solving of a constraint satisfaction problem, integrating prior knowledge in deformable models, and exploring images in a progressive fashion. Conversely, these models can be specified based on individual information resulting from the segmentation and recognition process. In particular models relying on spatial relations between structures are relevant and more flexible than shape models to be adapted to potential variations, multiple occurrences, or pathological cases. The problem of semantic gap is addressed by generating spatial representations (in the image space) of relations initially expressed in linguistic or symbolic form, within a fuzzy set formalism. This allows coping with uncertainty and fuzziness, which are inherent both to generic knowledge and to image information. Applications in medical imaging and remote sensing imaging illustrate the proposed paradigm.
机译：诸如本体和图形之类的结构模型可以对有关图像中观察到的场景的一般知识进行编码。它们在空间推理方案中的使用允许驱动分割和识别图像中的对象和结构。所开发的方法包括在图形中找到最佳分割路径，约束满足问题的全局求解，将先验知识整合到可变形模型中以及以渐进方式浏览图像。相反，可以基于由分割和识别过程产生的单个信息来指定这些模型。特别地，依赖于结构之间的空间关系的模型比形状模型更相关且更灵活，以适应潜在的变化，多次出现或病理情况。通过在模糊集形式主义内生成最初以语言或符号形式表示的关系的空间表示（在图像空间中），可以解决语义鸿沟的问题。这允许应付不确定性和模糊性，这是一般知识和图像信息所固有的。在医学成像和遥感成像中的应用说明了所提出的范例。

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