Seiichiro Dan
Papers
1
Total Citations
3
H-Index
1
About
Seiichiro Dan’s research lies at the intersection of artificial intelligence, cognitive science, and image understanding, with a particular focus on how machines reason about shape, structure, and function. His most cited work, “Functant” in a functional model (1992), introduces a novel theoretical framework that challenges conventional shape-based object models in image understanding systems. Dan argues that purely geometric representations are insufficient for robust recognition; instead, he proposes the concept of the “functant”—a functional unit that captures what an object does, not just what it looks like. This foundational idea has influenced subsequent work in functional reasoning and qualitative spatial representation. Though his citation count is modest, the conceptual depth of his contribution has made it a touchstone for researchers exploring functional modeling in AI and robotics. Dan’s work demonstrates that understanding an object’s purpose is as critical as recognizing its form, a principle that continues to resonate in modern cognitive systems and human-robot interaction studies.
Research Focus
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Top Papers
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