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Reconstruction of 3-Dimensional Histology Volume and its Application to Study Mouse Mammary Glands
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HAIRIS: a method for automatic image registration through histogram-based image segmentation.

Hernâni Gonçalves1, José Alberto Gonçalves, Luís Corte-Real

  • 1Departamento de Geociências, Ambiente e Ordenamento do Território, Faculdade de Ciências, Universidade do Porto, 4169-007 Porto, Portugal. hernani.goncalves@fc.up.pt

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
|September 16, 2010
PubMed
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A new method called HAIRIS uses histogram-based image segmentation for automatic image registration. This approach achieves subpixel accuracy for remote sensing images, overcoming challenges in rotation and translation registration.

Area of Science:

  • Computer Vision
  • Remote Sensing
  • Image Processing

Background:

  • Automatic image registration remains a significant challenge across various scientific fields.
  • Existing methods have limited adoption in applications like remote sensing.
  • Accurate image registration is crucial for multi-temporal and multi-sensor data analysis.

Purpose of the Study:

  • To propose a novel method for automatic image registration using histogram-based segmentation.
  • To address the limitations of current registration techniques in remote sensing applications.
  • To achieve high accuracy in registering images with rotational and translational differences.

Main Methods:

  • Developed the Histogram-based Image Registration (HAIRIS) method.
  • Combined multiple image segmentations based on histogram mode delineation with a relaxation parameter.

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  • Characterized segmented objects using area, ellipse axis ratio, perimeter, and fractal dimension.
  • Employed a robust statistical procedure for object matching.

    • Main Results:

    • Achieved accuracy below 1° for rotation and subpixel accuracy for translation in simulated datasets.
    • Demonstrated effectiveness on satellite imagery with varying spectral content and simulated translation.
    • Successfully applied to real-world remote sensing data from different sensors, dates, and viewing angles.
    • Validated HAIRIS's capability to register multi-temporal and multi-sensor images with spectral variations.

    Conclusions:

    • HAIRIS offers a robust solution for automatic image registration, particularly for remote sensing.
    • The method achieves high accuracy for images with rotational and translational differences.
    • HAIRIS demonstrates potential for broad application in analyzing diverse remote sensing datasets.