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Related Experiment Video

Updated: May 23, 2026

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
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Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Published on: October 10, 2018

Matching images using linear features.

G Medioni1, R Nevatia

  • 1Intelligent Systems Group, Departments of Electrical Engineering and Computer Science, University of Southern, California, Los Angeles, CA 90089.

IEEE Transactions on Pattern Analysis and Machine Intelligence
|April 14, 2012
PubMed
Summary
This summary is machine-generated.

This study introduces novel techniques for image and map matching using line-based descriptions and relaxation operations. These methods enhance object recognition and change detection in complex aerial imagery.

Related Experiment Videos

Last Updated: May 23, 2026

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
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Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Published on: October 10, 2018

Area of Science:

  • Computer Vision
  • Geospatial Analysis

Background:

  • Accurate image and map matching is crucial for various machine vision tasks.
  • Existing methods face challenges with diverse imaging conditions and structural changes.

Purpose of the Study:

  • To present advanced techniques for matching images or an image to a map.
  • To improve the robustness of matching algorithms in the presence of significant image variations.

Main Methods:

  • Utilizing line-based image descriptions for feature extraction.
  • Employing a relaxation operation to identify the most similar geometrical structures.
  • Introducing an efficient 'kernel' method variation for faster matching.

Main Results:

  • Demonstrated successful matching on complex aerial images with substantial differences.
  • Validated the system's performance despite variations in sun position, seasons, and imaging environments.
  • Showcased effectiveness in detecting man-made structural alterations.

Conclusions:

  • The developed line-based matching techniques are effective for complex aerial imagery.
  • The relaxation and kernel methods offer robust solutions for object recognition and change detection.
  • This work advances capabilities in automated map updating and passive navigation.