Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Force Classification01:22

Force Classification

1.3K
Forces play a crucial role in the study of physics and engineering. They are essential in describing the motion, behavior, and equilibrium of objects in the physical world. Forces can be classified based on their origin, type, and direction of action.
Contact and non-contact forces are two of the most widely used categories of forces. As the name suggests, contact forces require physical contact between two objects to act upon each other. Examples of contact forces include frictional,...
1.3K
Depth Perception and Spatial Vision01:15

Depth Perception and Spatial Vision

741
Depth perception is the ability to perceive objects three-dimensionally. It relies on two types of cues: binocular and monocular. Binocular cues depend on the combination of images from both eyes and how the eyes work together. Since the eyes are in slightly different positions, each eye captures a slightly different image. This disparity between images, known as binocular disparity, helps the brain interpret depth. When the brain compares these images, it determines the distance to an object.
741
Parallel Processing01:20

Parallel Processing

185
The brain processes sensory information rapidly due to parallel processing, which involves sending data across multiple neural pathways at the same time. This method allows the brain to manage various sensory qualities, such as shapes, colors, movements, and locations, all concurrently. For instance, when observing a forest landscape, the brain simultaneously processes the movement of leaves, the shapes of trees, the depth between them, and the various shades of green. This enables a quick and...
185
Deconvolution01:20

Deconvolution

197
Deconvolution, also known as inverse filtering, is the process of extracting the impulse response from known input and output signals. This technique is vital in scenarios where the system's characteristics are unknown, and they must be inferred from the observable signals.
Deconvolution involves several mathematical techniques to derive the impulse response. One common approach is polynomial division. In this method, the input and output sequences are treated as coefficients of...
197
Vision01:24

Vision

53.6K
Vision is the result of light being detected and transduced into neural signals by the retina of the eye. This information is then further analyzed and interpreted by the brain. First, light enters the front of the eye and is focused by the cornea and lens onto the retina—a thin sheet of neural tissue lining the back of the eye. Because of refraction through the convex lens of the eye, images are projected onto the retina upside-down and reversed.
53.6K
Light Acquisition02:16

Light Acquisition

8.5K
In order to produce glucose, plants need to capture sufficient light energy. Many modern plants have evolved leaves specialized for light acquisition. Leaves can be only millimeters in width or tens of meters wide, depending on the environment. Due to competition for sunlight, evolution has driven the evolution of increasingly larger leaves and taller plants, to avoid shading by their neighbors with contaminant elaboration of root architecture and mechanisms to transport water and nutrients.
8.5K

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

An Evidential Framework for Localization of Sensors in Indoor Environments.

Sensors (Basel, Switzerland)·2020
Same author

An Evaluation of the Pedestrian Classification in a Multi-Domain Multi-Modality Setup.

Sensors (Basel, Switzerland)·2015
Same author

Pedestrian detection in far-infrared daytime images using a hierarchical codebook of SURF.

Sensors (Basel, Switzerland)·2015
Same author

Vehicle detection by means of stereo vision-based obstacles features extraction and monocular pattern analysis.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2006

Related Experiment Video

Updated: Jul 24, 2025

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
03:31

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

Published on: December 15, 2023

583

Indoor Scene Recognition Mechanism Based on Direction-Driven Convolutional Neural Networks.

Andrea Daou1,2, Jean-Baptiste Pothin2, Paul Honeine1

  • 1Univ Rouen Normandie, INSA Rouen Normandie, Université Le Havre Normandie, Normandie Univ, LITIS UR 4108, F-76000 Rouen, France.

Sensors (Basel, Switzerland)
|July 8, 2023
PubMed
Summary

This study introduces a novel indoor localization system using deep learning and smartphone sensors. It enhances room-level accuracy by combining visual data with magnetic heading, improving upon traditional methods.

Keywords:
CNNsdeep learningdirection-drivenindoor localizationmagnetic headingmobile computation offloadingscene recognitionsmartphone sensors

More Related Videos

Deep Neural Networks for Image-Based Dietary Assessment
13:19

Deep Neural Networks for Image-Based Dietary Assessment

Published on: March 13, 2021

9.2K
Author Spotlight: Efficient Image Recognition Using Directional Gradient Histogram Technique and Support Vector Machines
08:27

Author Spotlight: Efficient Image Recognition Using Directional Gradient Histogram Technique and Support Vector Machines

Published on: January 5, 2024

1.2K

Related Experiment Videos

Last Updated: Jul 24, 2025

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
03:31

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

Published on: December 15, 2023

583
Deep Neural Networks for Image-Based Dietary Assessment
13:19

Deep Neural Networks for Image-Based Dietary Assessment

Published on: March 13, 2021

9.2K
Author Spotlight: Efficient Image Recognition Using Directional Gradient Histogram Technique and Support Vector Machines
08:27

Author Spotlight: Efficient Image Recognition Using Directional Gradient Histogram Technique and Support Vector Machines

Published on: January 5, 2024

1.2K

Area of Science:

  • Computer Vision
  • Indoor Localization
  • Deep Learning

Background:

  • Indoor location-based services are crucial for navigation and monitoring.
  • Vision-based scene recognition faces challenges due to complex indoor environments.
  • Existing systems struggle with variability in layouts, objects, and viewpoints.

Purpose of the Study:

  • To develop a room-level indoor localization system using deep learning and smartphone sensors.
  • To enhance accuracy by integrating visual information with magnetic heading.
  • To address computational limitations through a hybrid mobile computation offloading strategy.

Main Methods:

  • A direction-driven convolutional neural network (CNN) architecture with multiple CNNs for different orientations.
  • Weighted fusion strategies to combine outputs from various CNN models.
  • Hybrid computing approach splitting CNN implementation between smartphone and server.

Main Results:

  • The proposed system achieves improved room-level localization accuracy compared to traditional CNNs.
  • Weighted fusion strategies enhance overall system performance.
  • Model partitioning in hybrid mobile computation offloading proves beneficial.

Conclusions:

  • The developed system offers a robust and effective solution for indoor localization.
  • The integration of visual and magnetic data significantly boosts accuracy.
  • Hybrid mobile computation offloading is a viable strategy for resource-constrained devices.