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Related Experiment Video

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Reconstruction of Connected Digital Lines Based on Constrained Regularization.

Mojtaba Lashgari, Hossein Rabbani, Gerlind Plonka

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society

|August 17, 2022

Summary

This summary is machine-generated.

This study introduces a novel method for reconstructing disconnected digital lines (DDLs) by ensuring their connectivity. The approach effectively reconstructs missing pixels, improving line detection and binary image inpainting.

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Area of Science:

Computer Vision
Image Processing
Digital Geometry

Background:

Disconnected digital lines (DDLs) pose challenges in image analysis.
Existing methods for digital line reconstruction can lead to object destruction and blurring.

Purpose of the Study:

To present a new constrained regularization model for reconstructing disconnected digital lines (DDLs).
To ensure connectivity of digital lines (DLs) in the discrete image plane.
To improve digital line intersection detection and binary image inpainting.

Main Methods:

Determining the order of DDL pixels using 8-neighbor connectivity.
Estimating missing pixel coordinates via a constrained regularization model.
Transforming computed real values to ensure connectivity in the image plane.

Main Results:

The proposed method successfully reconstructs DDLs with guaranteed connectivity.
The approach enforces connected lines with minimal curvature.
Improved detection of DL intersections was observed.

Conclusions:

The novel constrained regularization approach effectively reconstructs disconnected digital lines.
This technique offers a fast and robust solution for binary image inpainting, overcoming limitations of conventional methods.