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

Visual System01:26

Visual System

2.1K
Light enters the eye through the cornea, a transparent, dome-shaped surface covering the surface of the eyeball that helps to direct and focus incoming light. This light is then channeled toward the pupil, an adjustable opening whose size is controlled by the iris. The iris, a pigmented muscle, regulates the amount of light entering the eye by contracting or dilating the pupil, thereby ensuring optimal light levels for clear vision.
Once through the pupil, the light passes through the lens, a...
2.1K
Depth Perception and Spatial Vision01:15

Depth Perception and Spatial Vision

2.3K
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.
2.3K

You might also read

Related Articles

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

Sort by
Same author

Effects of ticagrelor on proliferation, apoptosis, and inflammatory factors of human aortic vascular smooth muscle cells through lncRNA KCNQ1OT1.

American journal of translational research·2022
Same author

Effects of valproic acid on the susceptibility of human glioma stem cells for TMZ and ACNU.

Oncology letters·2018
Same author

A Smartphone Camera-Based Indoor Positioning Algorithm of Crowded Scenarios with the Assistance of Deep CNN.

Sensors (Basel, Switzerland)·2017
Same author

Using RNase sequence specificity to refine the identification of RNA-protein binding regions.

BMC genomics·2008
Same author

Identification of transcription factor and microRNA binding sites in responsible to fetal alcohol syndrome.

BMC genomics·2008
Same author

The role of amphiregulin in exemestane-resistant breast cancer cells: evidence of an autocrine loop.

Cancer research·2008
Same journal

RETRACTED: Zhang et al. A Novel Framework for Reconstruction and Imaging of Target Scattering Centers via Wide-Angle Incidence in Radar Networks. <i>Sensors</i> 2025, <i>25</i>, 6802.

Sensors (Basel, Switzerland)·2026
Same journal

Enhancing Unsupervised Multi-Source Domain Adaptation for Person Re-Identification via Mixture of Experts and Graph-Based Relation.

Sensors (Basel, Switzerland)·2026
Same journal

Development of an Instrumented Glove for Palmar Pressure Assessment in Kayakers.

Sensors (Basel, Switzerland)·2026
Same journal

Development and Experimental Validation of an Autonomous IoT-Based Monitoring System for Real-Time Water Quality Assessment in the Amazon River.

Sensors (Basel, Switzerland)·2026
Same journal

Semi-Supervised Adversarial Learning Framework for Controller Area Network Bus Intrusion Detection.

Sensors (Basel, Switzerland)·2026
Same journal

Smart Optimization Method for Safety Signs in Innovative Manufacturing Environments Integrating Industrial Field IoT Sensors and Knowledge Graphs.

Sensors (Basel, Switzerland)·2026
See all related articles

Related Experiment Video

Updated: Feb 27, 2026

Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment
08:25

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

Published on: May 7, 2019

9.7K

Build a Robust Learning Feature Descriptor by Using a New Image Visualization Method for Indoor Scenario Recognition.

Jichao Jiao1, Xin Wang2, Zhongliang Deng3

  • 1School of Electronic Engineering, Beijing University of Posts and Telecommunications, Beijing 100876, China. jiaojichao@bupt.edu.cn.

Sensors (Basel, Switzerland)
|July 6, 2017
PubMed
Summary
This summary is machine-generated.

Researchers developed an improved object detection method by combining Histogram of Oriented Gradient (HOG) features with Principal Component Analysis (PCA). This new HOG-PCA (HOGP) descriptor enhances accuracy by reducing background interference, closely mimicking human visual perception.

Keywords:
HOG feature descriptorfeature visualizationimage feature extractionindoor scenario recognitionsparse representation

More Related Videos

Author Spotlight: Assessment of Visual Acuity in Central Vision Loss Through Motion-Based Peripheral Vision Testing
06:25

Author Spotlight: Assessment of Visual Acuity in Central Vision Loss Through Motion-Based Peripheral Vision Testing

Published on: February 23, 2024

1.2K
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

1.7K

Related Experiment Videos

Last Updated: Feb 27, 2026

Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment
08:25

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

Published on: May 7, 2019

9.7K
Author Spotlight: Assessment of Visual Acuity in Central Vision Loss Through Motion-Based Peripheral Vision Testing
06:25

Author Spotlight: Assessment of Visual Acuity in Central Vision Loss Through Motion-Based Peripheral Vision Testing

Published on: February 23, 2024

1.2K
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

1.7K

Area of Science:

  • Computer Vision
  • Machine Learning
  • Image Processing

Background:

  • Object detection algorithms often struggle with background interference, leading to errors.
  • Visualizing Histogram of Oriented Gradient (HOG) features reveals discrepancies between computer and human perception.
  • HOG features capture rich texture information but are susceptible to background noise.

Purpose of the Study:

  • To improve the robustness of HOG features for object detection in indoor scenarios.
  • To develop a novel feature descriptor that reduces background interference.
  • To enhance the accuracy of object detection by aligning computer vision with human perception.

Main Methods:

  • Proposed an improved method by introducing Principal Component Analysis (PCA) to HOG features.
  • Extracted principal components of image color information using PCA.
  • Developed a hybrid feature descriptor, HOG-PCA (HOGP), by fusing HOG and PCA features.
  • Compared HOGP against the standard HOG feature descriptor.

Main Results:

  • HOGP feature visualization closely resembles human visual perception.
  • Qualitative and quantitative assessments show HOGP outperforms HOG in object detection.
  • The proposed HOGP method demonstrates improved robustness against background interference.
  • Runtime analysis indicates minimal increase compared to the classic HOG feature.

Conclusions:

  • The HOGP feature descriptor offers a significant improvement for object detection accuracy.
  • The fusion of HOG and PCA effectively suppresses background noise while retaining essential information.
  • The HOGP method provides a more human-like interpretation of image features for computer vision tasks.
  • This approach enhances object detection performance without substantially increasing computational cost.