arXiv Computer Vision
Playing with Kruskal: algorithms for flat and hierarchical watershed cuts
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arXiv Computer Vision
Sep 2
UI-VISA: U-Net Initialized Vascular Image Segmentation Architecture
arXiv:2609.01598v1 Announce Type: new Abstract: Accurate segmentation of vascular structures in digital subtraction angiography (DSA) images remains challenging due to the thin, elongated, and branch...
arXiv Machine Learning
Aug 11
Data-Driven Fire-Zone Segmentation for Improved Short-Term Wildfire Prediction
arXiv:2608. 07472v1 Announce Type: new Abstract: Wildfire prediction models typically discretize study areas into uniform grids, ignoring the heterogeneous spatial distribution of ignitions.
arXiv Computer Vision
Sep 23
Bayesian Fusion of Active Contour Models and ConvNet Priors for Standing Dead Tree Segmentation
arXiv:2410.07421v2 Announce Type: replace Abstract: Instance segmentation is a core computer vision task with great practical significance. Recent advances, driven by large-scale benchmark datasets,...
arXiv AI
Jun 16
A Comprehensive Survey of Medical Image Segmentation: Challenges, Benchmarks, and Beyond
arXiv:2606. 16153v1 Announce Type: cross Abstract: Medical image segmentation plays a critical role in clinical diagnostics, treatment planning, disease monitoring, and neurological disorder identification.
arXiv AI
Jul 7
SilvaScenes: Tree Detection and Species Classification from Under-Canopy Images in Natural Forests
arXiv:2510. 09458v2 Announce Type: replace-cross Abstract: Interest in forestry automation is growing alongside rapid advances in deep learning.
arXiv Computer Vision
Aug 31
Bringing SAM to new heights: Leveraging elevation data for tree crown segmentation from drone imagery
The paper introduces BalSAM, a model that combines the Segment Anything Model (SAM) with Digital Surface Model (DSM) elevation data to improve tree crown instance segmentation from high‑resolution drone imagery. Experiments across boreal plantations, temperate forests, and tropical forests show that while off‑the‑shelf SAM does not beat a custom Mask R-CNN, fine‑tuning SAM end‑to‑end and incorporating DSM information yield promising results, especially for plantation sites.