fragment segmentation

Existing fragment reassembly methods often rely on assumptions of regular geometric shapes, limiting their effectiveness in real-world scenarios—such as archaeology—where fragments exhibit highly irregular geometries. To address this, we propose a hybrid geometric-image compatibility modeling framework that requires no prior assumptions about shape, size, or content. Our approach comprises three key components: (1) a generative model simulating archaeological erosion to produce realistic irregular fragments; (2) a pairwise discriminative mechanism jointly optimizing edge-based geometric alignment and local texture feature matching; and (3) a puzzle-oriented, neighborhood-level evaluation metric. Integrated into an end-to-end archaeological jigsaw solving pipeline, our method achieves state-of-the-art performance on the RePAIR 2D benchmark—improving neighborhood accuracy by +4.2% and recall by +5.8%. It significantly enhances robustness and accuracy in compatibility assessment for geometrically complex fragments.

This paper addresses the challenging problem of automatic image fragment pairing and stitching for image restoration. We propose the first end-to-end jointly optimized framework that simultaneously solves fragment-pair search and geometric matching. Methodologically, we innovatively integrate a Graph Neural Network (GNN) with a linear Transformer to jointly model contour and texture features; we further introduce a dynamically weighted feature fusion module and a contrastive learning–driven global encoder, eliminating reliance on handcrafted rules and manual hyperparameter tuning. Experiments on our newly constructed irregular-fragment dataset demonstrate significant improvements: higher pairing accuracy, reduced geometric matching error, and substantially accelerated inference speed. Our core contributions include (1) unified modeling of pairing and alignment within a single architecture, (2) a learnable, adaptive feature fusion mechanism, and (3) an efficient, lightweight joint optimization paradigm that advances both accuracy and computational efficiency in fragment reconstruction.

Existing superpixel methods struggle to simultaneously achieve computational efficiency, boundary adherence, and superpixel regularity. To address this, we propose SCALP—a novel superpixel segmentation framework that introduces, for the first time, a linear path distance metric to replace conventional Euclidean distance within an iterative clustering scheme. This metric models the shortest contour-aware path from each pixel to its cluster centroid, thereby jointly optimizing compactness, regularity, and boundary conformity through geometric and semantic boundary co-constraints. Evaluated on the BSD dataset, SCALP outperforms all state-of-the-art methods: it achieves superior superpixel quality (measured by Undersegmentation Error and Boundary Recall) and state-of-the-art contour detection performance (ODS and OIS scores), while maintaining high computational efficiency. Our key contribution lies in breaking the inherent trade-off among compactness, regularity, and boundary adherence—establishing a new metric paradigm and efficient implementation for superpixel segmentation.

This study addresses the challenge of detecting subtle, low-contrast defects embedded within dense backgrounds in printed circuit board (PCB) inspection. To this end, the authors propose a two-stage detection framework: first, a structure-guided hybrid masked sparse convolution is employed for pretraining to learn structural priors inherent to PCB layouts; second, during fine-tuning, a spatial continuity regularization is introduced to enhance the compactness and coherence of predictions for elongated defect regions. By innovatively integrating structural prior modeling with spatial constraints, the method achieves state-of-the-art performance on the DsPCBSD+ dataset, attaining 85.5% mAP₀.₅ and 52.3% mAP₀.₅:₀.₉₅, significantly outperforming existing strong baselines.

Existing segmentation evaluation metrics often lack transparency and modularity, making them ill-suited for diverse tasks such as transparent object, specular surface, or lesion segmentation. This work proposes a unified evaluation framework that decomposes metrics into five modular components: prediction representation, target extraction, target matching, score computation, and metric reporting. For the first time, it systematically analyzes the implicit assumptions and design limitations of mainstream binary segmentation metrics through this modular lens. The framework enables task-aware customization of evaluation protocols, reveals evolutionary trajectories among existing metrics, and is accompanied by an open-source toolkit. By offering a principled and interpretable foundation, this approach paves the way for developing more rational and adaptable segmentation evaluation methodologies.

This study addresses the challenge of high-precision, non-invasive reconstruction of fragile paper fragments in cultural heritage by proposing a human–robot collaborative real-time reconstruction system. The system integrates a vacuum-based collaborative robot with the detector-free feature matching algorithm SE2-LoFTR, enabling vision-guided fragment alignment and assembly in either manual or fully automatic modes. It innovatively combines AI-driven analysis—leveraging image segmentation and local feature matching—with a safe vacuum gripper mechanism and high-accuracy robotic control. Experimental results demonstrate a repeatability positioning accuracy of 0.57 mm on fragments as small as 8 cm² and confirm the superior robustness of SE2-LoFTR under conditions involving rotation, scaling, and partial damage.

This work addresses the limitations of traditional minimal path-based image segmentation methods, which suffer from insufficient accuracy in complex backgrounds and high sensitivity to initialization. To overcome these issues, the authors propose a geodesic framework–based mask proposal voting mechanism that generates diverse and reliable mask candidates through adaptive domain partitioning and constructs the final segmentation via a voting strategy incorporating prior knowledge. The approach innovatively integrates region-based min-cut evolution, geodesic distance computation, and mask scoring maps, effectively eliminating dependence on initial seed points. Experimental results demonstrate that the proposed method significantly outperforms existing minimal path segmentation techniques across multiple challenging scenarios, achieving state-of-the-art performance in both robustness and accuracy.

This work addresses the challenges of high-resolution PCB defect detection, where naive global resizing often loses minute defects, while standard tiling-based inference introduces boundary artifacts that cause false negatives and positives. To overcome these issues, the authors propose a training-free, model-agnostic post-processing method termed Topology-Aware Tile Merging (TA-TM). Leveraging 640×640 tiles with 128-pixel overlaps, TA-TM constructs an adjacency graph to dynamically adjust detection scores near tile boundaries and refines results via global non-maximum suppression. Evaluated on the PCB-Defect and HRIPCB datasets, the approach achieves mAP@50 of 0.72 and 0.94, respectively, boosts boundary-region recall to 70–100%, and recovers 46–100% of small defects missed by full-image methods.