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Related Experiment Video

Updated: Aug 13, 2025

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

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Published on: August 30, 2013

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Hierarchical Image Transformation and Multi-Level Features for Anomaly Defect Detection.

Isack Farady1,2, Chia-Chen Kuo3, Hui-Fuang Ng4

  • 1Department of Electrical Engineering, Mercu Buana University, Jakarta 11650, Indonesia.

Sensors (Basel, Switzerland)
|January 21, 2023
PubMed
Summary

This study introduces Hierarchical Image Transformation and Multi-level Features (HIT-MiLF) to improve industrial anomaly detection. The method enhances training data to handle non-ideal images, boosting performance on metal datasets.

Failed At:

2026-06-19T13:39:58.214601+00:00

Keywords:
anomaly detectionfeature vectorimage transformationmetal defectpoison image

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