Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Fixation and Sectioning

Fixation and Sectioning

Two basic types of preparation are used to visualize specimens with a light microscope: wet mounts and fixed specimens.
The simplest type of preparation is the wet mount, in which the specimen is placed in a drop of liquid on the slide. A liquid specimen can be directly deposited on the slide using a dropper. Solid specimens, such as skin scraping, can be placed on the slide before adding a drop of liquid to prepare the wet mount. Sometimes the liquid is simply water, but stains are often added...

Association Areas of the Cortex

Association Areas of the Cortex

Association areas are regions of the cerebral cortex that do not have a specific sensory or motor function. Instead, they integrate and interpret information from various sources to enable higher cognitive processes such as memory, learning, and decision-making. Some key association areas include the following:
Prefrontal Association Area: This area is located in the frontal lobe and is involved in planning, decision-making, and moderating social behavior. It connects with primary motor areas,...

You might also read

Related Articles

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

Sort by

Same author

GEOMETRY OF LONG-TAILED REPRESENTATION LEARNING: REBALANCING FEATURES FOR SKEWED DISTRIBUTIONS.

... International Conference on Learning Representations·2026

Same author

MIST: A Benchmark and Baseline for Multi-Frame Infrared Small Target Detection in Complex Motion.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2026

Same author

Reparameterization-Driven Depthwise Separable Large-Kernel Network for Lightweight Salient Object Detection of Strip Steel Surface Defects.

IEEE transactions on cybernetics·2026

Same author

Setup-Independent Full Projector Compensation.

IEEE transactions on visualization and computer graphics·2026

Same author

DiffPC: Diffusion-Based Projector Photometric Compensation.

IEEE transactions on visualization and computer graphics·2026

Same author

The CXCL13 hub in obesity-related metabolic inflammation: regulation of spatiotemporal heterogeneity and targeted strategies.

The Journal of endocrinology·2026

Same journal

Change-Prior-Guided Unsupervised Change Detection of Heterogeneous Remote Sensing Images.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2026

Same journal

AgonicDreamer: Enhancing Multi-View Consistency in Text-to-3D Generation via Rectified Score Distillation.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2026

Same journal

BiCM-Prompt: Bidirectional Cross-Modal Prompt Tuning for Class-Incremental Learning on Multisource Remote Sensing Images.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2026

Same journal

GoP-based Quality Enhancement on Video Compression.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2026

Same journal

Align then Tensorize: Multi-Level Consistent Anchor Graph Learning for Scalable Multi-View Clustering.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2026

Same journal

Beyond Fidelity: Diverse Image Synthesis via Retrieval-Augmented Diffusion.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Nov 25, 2025

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Published on: July 5, 2024

Personal Fixations-Based Object Segmentation With Object Localization and Boundary Preservation.

Gongyang Li, Zhi Liu, Ran Shi

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society

|December 18, 2020

Summary

This summary is machine-generated.

This study introduces a new dataset and the Object Localization and Boundary Preservation (OLBP) network for Personal Fixations-based Object Segmentation (PFOS). The OLBP network effectively segments gazed objects using fixation data, outperforming existing methods.

More Related Videos

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

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

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

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

Related Experiment Videos

Last Updated: Nov 25, 2025

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Published on: July 5, 2024

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

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

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

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

Area of Science:

Computer Vision
Human-Computer Interaction
Machine Learning

Background:

Interactive image segmentation using human fixation is a promising HCI approach.
Previous studies lacked adequate datasets and struggled with fixation ambiguity.
Personal Fixations-based Object Segmentation (PFOS) requires specialized methods.

Purpose of the Study:

To address limitations in existing PFOS methods by creating a new dataset and proposing an advanced segmentation network.
To improve the accuracy and completeness of segmenting objects based on user fixations.

Main Methods:

Constructed a novel PFOS dataset with pixel-level annotations.
Developed the Object Localization and Boundary Preservation (OLBP) network.
Incorporated an Object Localization Module (OLM) and a Boundary Preservation Module (BPM) into the OLBP network.
Employed a mixed bottom-up and top-down architecture with deep supervision.

Main Results:

The proposed OLBP network demonstrated superior performance compared to 17 state-of-the-art methods on the new PFOS dataset.
Experimental results validated the effectiveness of the OLM and BPM components.
The new PFOS dataset facilitates further research in fixation-based segmentation.

Conclusions:

The developed PFOS dataset and OLBP network represent significant advancements in interactive image segmentation.
The OLBP network provides a robust solution for segmenting gazed objects using personal fixations.
Future research can leverage the dataset and network for more intuitive human-computer interaction systems.