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

Parallel Processing01:20

Parallel Processing

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...

You might also read

Related Articles

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

Sort by
Same author

Developing a Standardised National Model of Care for Treatment of Peanut Allergy in Infants: The ADAPT Peanut Oral Immunotherapy Program.

Clinical and experimental allergy : journal of the British Society for Allergy and Clinical Immunologyยท2025
Same author

Reducing the Delay in the Diagnosis of Bipolar Disorder: A Qualitative Study.

Health expectations : an international journal of public participation in health care and health policyยท2025
Same author

HIV Knowledge, Risk Behaviors, and Testing Among Chinese-, Korean-, and Vietnamese-American Women.

Journal of health science & educationยท2024
Same author

Morbidity and mortality in Danio rerio and Pimephales promelas exposed to antilipidemic drug mixtures (fibrates and statins) during embryogenesis: Comprehensive assessment via ante and post mortem endpoints.

Chemosphereยท2020
Same author

I have heard about it for the first time from you! Implementation of tobacco control law by police personnel in India.

Public health actionยท2019
Same author

The effectiveness of computed tomography-guided biopsy for the diagnosis of spondylodiscitis: an analysis of variables affecting the outcome.

European review for medical and pharmacological sciencesยท2017
Same journal

TraGraph-GS: Trajectory Graph-based Gaussian Splatting for Arbitrary Large-Scale Scene Rendering.

IEEE transactions on pattern analysis and machine intelligenceยท2026
Same journal

SWIFT: A Small-World Interaction Framework for Flow-Aware Trajectory Prediction in Autonomous Driving.

IEEE transactions on pattern analysis and machine intelligenceยท2026
Same journal

HardFlow: Hard-Constrained Sampling for Flow-Matching Models Via Trajectory Optimization.

IEEE transactions on pattern analysis and machine intelligenceยท2026
Same journal

Industrial Brain: Self-Evolving Neuro-Symbolic Autonomy with Causal Resilience for Cyber-Physical Systems.

IEEE transactions on pattern analysis and machine intelligenceยท2026
Same journal

Adaptive Hardness-Driven Dictionary Distillation for Incomplete Streaming View Clustering.

IEEE transactions on pattern analysis and machine intelligenceยท2026
Same journal

Mixture of Global and Local Experts with Diffusion Transformer for Controllable Face Generation.

IEEE transactions on pattern analysis and machine intelligenceยท2026
See all related articles

Related Experiment Video

Updated: May 29, 2026

Analysis of Multidimensional Microscopy Data Using Cell-ACDC
06:17

Analysis of Multidimensional Microscopy Data Using Cell-ACDC

Published on: November 7, 2025

Multiprocessor pyramid architectures for bottom-up image analysis.

N Ahuja1, S Swamy

  • 1Coordinated Science Laboratory, University of Illinois at Urbana-Champaign, Urbana, IL 61801.

IEEE Transactions on Pattern Analysis and Machine Intelligence
|August 27, 2011
PubMed
Summary
This summary is machine-generated.

This study introduces three processor organizations for bottom-up image analysis: pyramids, interleaved pyramids, and pyramid trees. These methods enable efficient image processing by transmitting quadrant border information between hierarchical levels.

More Related Videos

Long-Term Imaging of Identified Neural Populations using Microprisms in Freely Moving and Head-Fixed Animals
06:25

Long-Term Imaging of Identified Neural Populations using Microprisms in Freely Moving and Head-Fixed Animals

Published on: January 19, 2024

Related Experiment Videos

Last Updated: May 29, 2026

Analysis of Multidimensional Microscopy Data Using Cell-ACDC
06:17

Analysis of Multidimensional Microscopy Data Using Cell-ACDC

Published on: November 7, 2025

Long-Term Imaging of Identified Neural Populations using Microprisms in Freely Moving and Head-Fixed Animals
06:25

Long-Term Imaging of Identified Neural Populations using Microprisms in Freely Moving and Head-Fixed Animals

Published on: January 19, 2024

Area of Science:

  • Computer Vision
  • Image Processing
  • Parallel Computing

Background:

  • Hierarchical image processing methods are crucial for efficient analysis.
  • Bottom-up image analysis requires effective methods for handling interactions between image regions.

Purpose of the Study:

  • To describe and compare three hierarchical processor organizations for bottom-up image analysis: pyramids, interleaved pyramids, and pyramid trees.
  • To illustrate how these organizations facilitate bottom-up analysis through the transmission of quadrant border information.

Main Methods:

  • Describing three hierarchical processor organizations: pyramids, interleaved pyramids, and pyramid trees.
  • Illustrating algorithm operations (area, perimeter, connected component counting) on these structures.
  • Analyzing performance measures including processor utilization and throughput.

Main Results:

  • Pyramids, interleaved pyramids, and pyramid trees offer different trade-offs between hardware, processing time, and processor utilization.
  • Interleaved pyramids enhance utilization and throughput but require more hardware.
  • Pyramid trees reduce hardware needs for large structures with minimal impact on utilization.

Conclusions:

  • The three described organizations provide viable strategies for bottom-up image analysis.
  • The choice of organization depends on specific hardware and performance requirements.
  • Border-related information is key for capturing quadrant interactions in hierarchical image analysis.