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Knowledge-Driven Superpixel Shortest Path Optimization for Image Stitching
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DOI：10.1007/978-981-95-3072-4_9.png)
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Abstract
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UAV and remote sensing image stitching has significant value in agricultural monitoring, environmental surveillance, and urban inspection. However, traditional stitching methods often suffer from visual artifacts caused by imperfect seam-line selection, as well as inefficiency and limited precision in complex scenarios. This paper proposes a knowledge-driven seamline optimization framework that integrates superpixel segmentation, edge saliency analysis, and shortest-path algorithms. First, SLIC superpixel segmentation partitions images into homogeneous regions, reducing computational redundancy through semantic-aware knowledge modeling and improving computational efficiency through adaptive superpixel seed distribution, priority queue acceleration, and parallelization. Second, edge saliency analysis extracts high-frequency structural features as prior knowledge to avoid seamlines crossing prominent edges. Finally, a multi-feature fusion mechanism combining color consistency, edge significance, and spatial continuity guides the Dijkstra shortest-path algorithm to optimize seamline placement for natural transitions, enabling knowledge-based integration of heterogeneous visual cues. Furthermore, the proposed method incorporates a parallelized shortest-path search and dynamically adjusted superpixel constraints to further refine seamline placement. Experimental results on multiple high-resolution remote sensing and UAV datasets demonstrate that the method not only enhances the perceptual quality of stitched images but also effectively reduces visible artifacts.
Keywords:
Superpixel segmentation
Edge saliency analysis
Multi-feature fusion
Seamline path planning
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