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Automatic Quaternion-Domain Color Image Stitching.

Jiaxue Li, Yicong Zhou

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

|February 8, 2024

Summary

This summary is machine-generated.

This study introduces a novel quaternion-based method for color image stitching, optimizing seamline detection and quality assessment in the quaternion domain for superior results.

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Area of Science:

Computer Vision
Image Processing
Computational Mathematics

Background:

Traditional image stitching methods often struggle with color inconsistencies and artifacts.
Color image stitching requires robust seamline detection and quality evaluation.

Purpose of the Study:

To develop a novel framework for automatic color image stitching entirely within the quaternion domain.
To introduce a quaternion perceptual seamline detection model and quality measure.

Main Methods:

Utilizing quaternion representation for color images.
Formulating seamline detection as a quaternion-domain image labeling problem.
Minimizing local-area quaternion perceptual difference for optimal seamline generation.
Developing a quaternion perceptual seamline quality measure.
Proposing an automatic quaternion-domain color image stitching (AQCIS) framework with joint local alignment and seamline optimization.

Main Results:

The proposed AQCIS framework achieves superior performance in color image stitching.
The quaternion perceptual seamline detection model effectively identifies optimal seamlines.
The developed quality measure accurately assesses seamline quality.
Experimental results on challenging datasets validate the effectiveness of AQCIS compared to state-of-the-art methods.

Conclusions:

This work presents the first complete color image stitching solution in the quaternion domain.
The AQCIS framework offers a robust and effective approach to automatic color image stitching.
The quaternion-based approach significantly enhances stitching quality and reduces artifacts.