Adam Kortylewski

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Footwear impressions are one of the most frequently secured types of evidence at crime scenes. For the investigation of crime series they are among the major investigative notes. In this paper, we introduce an unsupervised footwear retrieval algorithm that is able to cope with unconstrained noise conditions and is invariant to rigid transformations. A main(More)
Hierarchical compositional models (HCMs) have shown impressive generalisation capabilities, especially compared to the small amounts of data needed for training. However, regarding occlusion and other non-linear pattern distortions, experimental setups have been controlled so far. In this work, we study the robustness of HCMs under such more challenging(More)
This paper proposes to integrate a feature pursuit learning process into a greedy bottom-up learning scheme. The algorithm combines the benefits of bottom-up and top-down approaches for learning hierarchical models: It allows to induce the hierarchical structure of objects in an unsupervised manner, while avoiding a hard decision on the activation of parts.(More)
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