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Photorealistic Learned Landscapes for Augmented Reality
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Real-Time Indoor Scene Description for the Visually Impaired Using Autoencoder Fusion Strategies with Visible

Salim Malek1, Farid Melgani2, Mohamed Lamine Mekhalfi3

  • 1Department of Information Engineering and Computer Science, University of Trento, Via Sommarive 9, I-38123 Trento, Italy. salim.malek@unitn.it.

Sensors (Basel, Switzerland)
|November 17, 2017
PubMed
Summary
This summary is machine-generated.

This study introduces image description strategies for visually impaired individuals to perceive indoor environments. Fusing learned features improves object recognition accuracy and enables faster, real-time applications.

Keywords:
assistive technologiescoarse scene descriptiondeep learningfeature fusionimage representationmultiobject recognitionvisible camerasvisually impaired (VI) people

Related Experiment Videos

Last Updated: Feb 18, 2026

Photorealistic Learned Landscapes for Augmented Reality
06:54

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Area of Science:

  • Computer Vision
  • Artificial Intelligence
  • Assistive Technology

Background:

  • Visually impaired individuals face challenges in perceiving their indoor surroundings.
  • Existing assistive technologies often lack real-time object recognition capabilities.

Purpose of the Study:

  • To develop and evaluate coarse image description strategies for enhanced indoor environment perception.
  • To improve object recognition accuracy and processing speed for assistive applications.

Main Methods:

  • Utilized chest-mounted cameras for image acquisition.
  • Implemented color, texture, and shape-based feature extraction.
  • Employed AutoEncoder (AE) models for feature learning and fusion.
  • Applied a multilabel classifier for object listing.

Main Results:

  • Fusing AE-learned features significantly improved classification rates compared to individual features.
  • The proposed method achieved higher classification accuracies than reference works.
  • The system demonstrated a processing speed at least four times faster than existing methods.

Conclusions:

  • The developed image description strategies effectively enhance indoor environment perception for the visually impaired.
  • Feature fusion and AE-based learning are crucial for high-accuracy, real-time object recognition.
  • The system's speed enables potential real-time application in assistive technology.