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Harmonising Observations and Underlying Principles for Visual Data Association

CORDIS · observation · Publication date unknown

or matching collections of 3D shapes for medical analysis. Despite the high relevance of visual data association, its progress has not kept pace with the revolutionary developments fuelled by recent deep learning advances: existing data association machinery lacks theoretical guarantees (e.g. global optimality, or structure such as geometric consistency in 3D shape matching) that are critical for high-stakes settings, or suffers from poor scalability. Moreover, current procedures fall short of understanding complex interconnections across different observable entities (collections of e.g. objects or scenes). The vision of Harmony is to tackle these shortcomings by harmonising the complex interconnections between observable entities and underlying fundamental principles (e.g. geometry, or physics). This research direction is challenging, largely unexplored and will require to break substantially new ground at conceptual, algorithmic and practical levels simultaneously. Harmony is organised into four complementary challenges: Challenge A addresses global optimality and scalability for

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recordType

award

status

SIGNED

region

EU

value

1624911

unit

EUR