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 Video

Updated: May 29, 2026

Cross-Modal Multivariate Pattern Analysis
13:51

Cross-Modal Multivariate Pattern Analysis

Published on: November 9, 2011

Pattern recognition experiments in the mandala/cosine domain.

Y S Hsu1, S Prum, J H Kagel

  • 1Radar Systems Group, Hughes Aircraft Company, Los Angeles, CA 90009.

IEEE Transactions on Pattern Analysis and Machine Intelligence
|August 27, 2011
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

Bliss points vs. minimum needs: Tests of competing motivational models.

Behavioural processes·2014
Same author

Image approximation by variable knot bicubic splines.

IEEE transactions on pattern analysis and machine intelligence·2011
Same author

Rupture of the Bladder and Enteritis.

British medical journal·2010
Same author

Monochrome digital image enhancement.

Applied optics·2010
Same author

Digital fourier transforms as means for scanner evaluation.

Applied optics·2010
Same author

Data compression and enhancement of sampled images.

Applied optics·2010
Same journal

A Unified and Fast-Sampling Diffusion Bridge Framework via Stochastic Optimal Control.

IEEE transactions on pattern analysis and machine intelligence·2026
Same journal

Robust 3D Semantic Occupancy Prediction With Calibration-Free Spatial Transformation.

IEEE transactions on pattern analysis and machine intelligence·2026
Same journal

Image Restoration via Multi-domain Learning.

IEEE transactions on pattern analysis and machine intelligence·2026
Same journal

A Comprehensive Survey on Multimodal Recommender Systems: Taxonomy, Evaluation, and Future Directions.

IEEE transactions on pattern analysis and machine intelligence·2026
Same journal

Benchmarking the Robustness of Autonomous Driving to Environmental Illusions: A Lane Perception Perspective.

IEEE transactions on pattern analysis and machine intelligence·2026
Same journal

Learning Topology-Aware Representations via Test-Time Adaptation for Anomaly Segmentation.

IEEE transactions on pattern analysis and machine intelligence·2026
See all related articles

This study introduces a novel method for object recognition in images by combining image compression and automatic target recognition. The technique achieves high classification accuracy, effectively distinguishing between natural and man-made objects using transformed image features.

Area of Science:

  • Computer Vision
  • Image Processing
  • Signal Processing

Background:

  • Object recognition in images is crucial for applications like automatic target recognition (ATR).
  • Simultaneous image bandwidth compression and feature extraction are desirable for efficient processing.
  • Existing methods may not optimally balance compression and recognition performance.

Purpose of the Study:

  • To investigate object recognition using features from the block cosine transform domain.
  • To develop an efficient method for automatic target recognition that integrates data compression.
  • To evaluate the effectiveness of Mandala sorting within the block cosine domain for feature selection.

Main Methods:

  • Utilized the block cosine transform for image data compression and feature extraction.

More Related Videos

Generating Strictly Controlled Stimuli for Figure Recognition Experiments
05:39

Generating Strictly Controlled Stimuli for Figure Recognition Experiments

Published on: March 18, 2019

Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns
13:44

Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns

Published on: August 30, 2013

Related Experiment Videos

Last Updated: May 29, 2026

Cross-Modal Multivariate Pattern Analysis
13:51

Cross-Modal Multivariate Pattern Analysis

Published on: November 9, 2011

Generating Strictly Controlled Stimuli for Figure Recognition Experiments
05:39

Generating Strictly Controlled Stimuli for Figure Recognition Experiments

Published on: March 18, 2019

Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns
13:44

Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns

Published on: August 30, 2013

  • Applied Mandala sorting to the block cosine domain for enhanced feature representation.
  • Developed features based on correlation and homogeneity measures for object discrimination.
  • Employed the Bhattacharyya feature discriminator for feature space compression (10:1 ratio).
  • Implemented Gaussian and minimum distance classification algorithms.

    • Main Results:

    • Mandala sorting proved effective for selecting target identification parameters.
    • Features derived from the Mandala/cosine domain successfully discriminated between natural and man-made objects.
    • Achieved 10:1 compression of the feature space.
    • Demonstrated successful classification of targets like ships against clouds in visible spectrum imagery.
    • Experimental verification on 38 images yielded classification results in the high 80s to low 90 percentile range.

    Conclusions:

    • The proposed method effectively integrates image compression with automatic target recognition.
    • The Mandala/cosine domain offers a superior feature space for object discrimination.
    • The technique shows promise for real-time ATR applications with significant data compression benefits.