发表机构
University of Turku（图尔库大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出域父分组（DPG）方法，通过多域跨域重叠实现无语义先验的非排他性视觉分组，实验证明其能形成与低级图像结构及人类标注对应的分组。
AI 中文摘要
大多数计算机视觉系统将视觉输入组织为预定义的解读，例如语义类别、提示区域、学习到的类对象表示或单一的空间分割。本研究考虑视觉组织的更早阶段：在身份、意义或任务相关性已知之前，直接从传感器测量中形成候选感知单元。我们引入域父分组（DPG），一种基于传感器的分组方法，其中互补的测量关系被表示在独立的处理域中。这些域内形成的空间连接组通过跨域重叠相关联，产生非排他性的分组表示，而非单一的互斥分割。这种表示同时保留更广泛和更局部的组，以及同一图像位置上的替代分组边界。DPG还包含一种原生机制，用于重新处理选定的组内容，其中输入相对测量范围允许观测分辨率改变，同时保留先前形成的组。DPG使用三个域实现，分别表示局部情境化的亮度、直接色度关系和情境色度关系。在BSDS500数据集上的实验证明了结合这三个域的好处。结果进一步表明，DPG形成与低级图像结构对应的测量支持组，并且这些组与人类标注的区域和边界表现出可测量的对应关系。这表明结构化的视觉组织可以直接从传感器测量之间的关系中产生。
英文摘要
Most computer-vision systems organize visual input toward a predefined interpretation, such as semantic categories, prompted regions, learned object-like representations, or a single spatial partition. This work considers an earlier stage of visual organization: the formation of candidate perceptual units directly from sensor measurements before their identity, meaning, or task relevance is known. We introduce Domain Parent Grouping (DPG), a sensor-grounded grouping method in which complementary measurement relationships are represented in separate processing domains. Spatially connected groups formed within these domains are related through cross-domain overlap, yielding a non-exclusive grouping representation rather than a single mutually exclusive segmentation. This representation retains broader and more localized groups, as well as alternative grouping boundaries over the same image locations, simultaneously available. DPG also includes a native mechanism for reprocessing selected group content, in which input-relative measurement ranges allow the observational resolution to change while preserving previously formed groups. DPG is implemented using three domains representing locally contextualized luminance, direct chromatic relationships, and contextual chromatic relationships. Experiments on the BSDS500 dataset demonstrate the benefit of combining the three domains. The results further show that DPG forms measurement-supported groups corresponding to low-level image structure, and that these groups exhibit measurable correspondence with human-annotated regions and boundaries. This demonstrates that structured visual organization can emerge directly from relationships among sensor measurements.
Comments39 pages, 13 figures
