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Visual Agnosia

Visual Agnosia

Visual agnosia is a condition characterized by the inability to recognize visually presented objects despite having normal vision. For instance, a person with visual agnosia can describe the shape and color of an object but cannot identify or name it. This impairment does not affect their visual field, acuity, color vision, brightness discrimination, language, or memory. An example of this condition in a social setting is someone at a dinner party asking for "that silver thing with a round...

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Related Experiment Video

Updated: Aug 5, 2025

A Standardized Obstacle Course for Assessment of Visual Function in Ultra Low Vision and Artificial Vision

A Standardized Obstacle Course for Assessment of Visual Function in Ultra Low Vision and Artificial Vision

Published on: February 11, 2014

Indoor Signs Detection for Visually Impaired People: Navigation Assistance Based on a Lightweight Anchor-Free Object

Yahia Said^1,2,3, Mohamed Atri⁴, Marwan Ali Albahar⁵

¹Remote Sensing Unit, College of Engineering, Northern Border University, Arar 91431, Saudi Arabia.

International Journal of Environmental Research and Public Health

|March 29, 2023

Summary

This study introduces FAM-centerNet, an improved object detection model for indoor navigation aids. It enhances sign detection for visually impaired individuals, improving accessibility in complex environments.

Keywords:

deep learning disabilities indoor signs navigation assistance object detection visually impaired

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Last Updated: Aug 5, 2025

A Standardized Obstacle Course for Assessment of Visual Function in Ultra Low Vision and Artificial Vision

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Published on: February 11, 2014

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

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Published on: December 15, 2023

Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment

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Published on: May 7, 2019

Area of Science:

Computer Vision
Artificial Intelligence
Assistive Technology

Background:

Navigating indoor environments poses challenges for visually impaired individuals.
Effective indoor sign detection is crucial for providing navigation assistance.

Purpose of the Study:

To propose FAM-centerNet, a lightweight anchor-free object detection model for enhanced indoor sign detection.
To improve the navigation capabilities for visually impaired people.

Main Methods:

Utilized CenterNet as a baseline anchor-free object detection model.
Introduced a Foreground Attention Module (FAM) for feature extraction in complex backgrounds.
Employed midground proposal and boxes-induced segmentation for foreground object feature extraction.

Main Results:

FAM-centerNet demonstrated efficiency in detecting general objects on the Pascal VOC dataset.
The model showed strong performance in detecting custom indoor signs on a dedicated dataset.
The Foreground Attention Module significantly enhanced the baseline model's performance.

Conclusions:

FAM-centerNet is an effective model for indoor sign detection, particularly for assistive technologies.
The Foreground Attention Module improves object detection accuracy and scale information regression.
The proposed method contributes to better indoor navigation solutions for the visually impaired.