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 Experiment Videos

Color object detection using spatial-color joint probability functions.

Jiebo Luo1, David Crandall

  • 1Research and Development Laboratories, Eastman Kodak Company, Rochester, NY 14650-1816, USA. jiebo.luo@kodak.com

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
|June 13, 2006
PubMed
Summary
This summary is machine-generated.

Related Concept Videos

You might also read

Related Articles

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

Sort by
Same author

Latent Chain-of-Thought for Visual Reasoning.

Advances in neural information processing systems·2026
Same author

Procedure-Aware Hierarchical Alignment for Open Surgery Video-Language Pretraining.

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

Towards Automated Reporting: A Bronchoscopy Report Dataset for Enhancing Multimodality Large Language Models.

Scientific data·2026
Same author

Time-Series Machine Learning for Prediction of Bronchopulmonary Dysplasia.

The Journal of pediatrics·2026
Same author

Time series analysis of impact of COVID-19 on infant and neonatal mortality in the United States.

Pediatric research·2025
Same author

End-to-End Open-Vocabulary Video Visual Relationship Detection Using Multi-Modal Prompting.

IEEE transactions on pattern analysis and machine intelligence·2025
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

This study introduces a robust algorithm for detecting compound color objects using a single model image. The novel approach excels in object recognition despite rotation, scaling, and partial occlusion.

Area of Science:

  • Computer Vision
  • Image Understanding
  • Pattern Recognition

Background:

  • Object detection in unconstrained images is challenging, often requiring object-specific algorithms and extensive data or manual tuning.
  • Existing methods struggle with arbitrary object detection, necessitating customization for each new object class.

Purpose of the Study:

  • To develop a robust algorithm for detecting compound color objects from a single model image.
  • To address limitations in current object detection methods for unconstrained environments.

Main Methods:

  • Utilized a spatial-color joint probability function: the color edge co-occurrence histogram.
  • Incorporated perceptual color naming to manage color variations.
  • Employed prescreening techniques to optimize search scope (size and location).

Related Experiment Videos

Main Results:

  • The algorithm demonstrated robustness against object rotation, scaling, partial occlusion, and folding.
  • Achieved superior performance compared to a related algorithm based on color co-occurrence histograms.

Conclusions:

  • The proposed algorithm offers a significant advancement in detecting compound color objects in complex, unconstrained images.
  • The method provides a more generalizable and less data-intensive solution for object recognition tasks.