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Deep Neural Networks for Image-Based Dietary Assessment
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Texture and art with deep neural networks.

Leon A Gatys1, Alexander S Ecker2, Matthias Bethge3

  • 1Werner Reichardt Centre for Integrative Neuroscience and Institute of Theoretical Physics, University of Tübingen, Germany; Bernstein Center for Computational Neuroscience, Tübingen, Germany; Graduate School for Neural Information Processing, Tübingen, Germany.

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Recent neuroscience-inspired texture synthesis advances significantly improved computer vision image manipulation using convolutional neural networks (CNNs), potentially guiding future visual perception research.

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Area of Science:

  • Interdisciplinary research bridging biological vision and computer vision.
  • Focus on visual information processing and computational neuroscience.
  • Leveraging insights from visual neuroscience for artificial intelligence.

Background:

  • Biological vision and computer vision have a long history of mutual influence.
  • Texture synthesis is a key area where neuroscience has impacted computer vision.
  • Convolutional neural networks (CNNs) are central to modern image synthesis and manipulation.

Purpose of the Study:

  • To review recent advances in texture synthesis driven by visual neuroscience.
  • To discuss the impact of these advances on computer vision, particularly CNNs.
  • To explore how these computer vision developments can inspire new research in visual perception.

Main Methods:

  • Review of recent literature on texture synthesis and its neuroscience origins.
  • Analysis of how neuroscience-inspired methods have advanced CNN-based image synthesis.
  • Discussion of the reciprocal relationship between computer vision and neuroscience.

Main Results:

  • Neuroscience-motivated texture synthesis has led to significant progress in image synthesis and manipulation.
  • Convolutional neural networks (CNNs) have been instrumental in implementing these advances.
  • The review highlights a successful cross-disciplinary application of biological vision principles.

Conclusions:

  • Recent advances demonstrate the power of integrating visual neuroscience with computer vision.
  • These findings offer a foundation for future research at the intersection of perception and AI.
  • The study underscores the ongoing value of interdisciplinary approaches in advancing both fields.