Publications by authors named "M Dujmovic"

Localisation of simple stimuli such as angle vertices may contribute to a plethora of illusory effects. We focus on the Müller-Lyer illusion in an attempt to measure and characterise a more elementary effect that may contribute to the magnitude of said illusion. Perceived location error of angle vertices (a single set of Müller-Lyer fins) and arcs in a 2D plane was measured with the aim to provide clarification of ambiguous results from studies of angle localisation and expand the results to other types of stimuli.

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Introduction: More than ever, traditional agricultural practices need a shift towards more resilient, sustainable, modern and adaptable practices that benefit the health of the planet and people. Today's consumers are constantly on the lookout for novel, highly nutritious foods that have a positive impact on their overall health and well-being. Nettle (Urtica dioica L.

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Research Background: As food production faces major challenges, modern agricultural practices are increasingly focused on conserving resources, reducing negative environmental impacts and sustainably producing food with a high content of health-promoting phytochemicals. During production, many factors can affect the quality and chemical composition of a final food product. Proper selection of cultivating conditions, especially a balanced nutrition, can significantly increase nutritional value and result in foods with strong biological and functional properties.

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On several key issues we agree with the commentators. Perhaps most importantly, everyone seems to agree that psychology has an important role to play in building better models of human vision, and (most) everyone agrees (including us) that deep neural networks (DNNs) will play an important role in modelling human vision going forward. But there are also disagreements about what models are for, how DNN-human correspondences should be evaluated, the value of alternative modelling approaches, and impact of marketing hype in the literature.

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Article Synopsis
  • Humans are naturally sensitive to the relationships between parts of objects, which may help in classifying different object categories.
  • A study comparing human perception to that of convolutional neural networks (CNNs) found that while humans prioritized relational changes, CNNs treated all changes similarly and lacked this relational sensitivity.
  • The research concludes that human shape representations are fundamentally different from those of CNNs because humans interpret relational changes in a way that connects visual input to the understanding of distal objects, a process that CNNs do not replicate.
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