Local-Adaptive Image Alignment Based on Triangular Facet Approximation.

Area of Science:

• Computer Vision
• Image Processing
• Computational Geometry

Background:

• Accurate and efficient image alignment is crucial for panoramic stitching.
• Existing methods often rely on camera imaging models, limiting robustness.

Purpose of the Study:

• To propose a novel local-adaptive image alignment method for panoramic stitching.
• To improve robustness and efficiency compared to existing techniques.

Main Methods:

• Developed a local-adaptive image alignment method based on triangular facet approximation.
• Introduced a robust planar transformation model extended with local-adaptive weighting strategies.
• Implemented planar and spherical triangulation for normal and fisheye images, respectively.

Main Results:

• The proposed method directly manipulates matching data in camera coordinates, outperforming traditional imaging models.
• Comparative experiments demonstrated the method's qualitative and quantitative efficiency on challenging datasets.

Conclusions:

• The local-adaptive triangular facet approximation offers a robust and efficient solution for image alignment in panoramic stitching.
• The method effectively handles both normal and fisheye images, advancing the field.