arXiv Computer Vision
Ideal Observer for Segmentation of Dead Leaves Images
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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 Computer Vision
Sep 23
Latent Commonality Expectation-Maximisation for Box-supervised Tree Crown Instance Segmentation
arXiv:2609.26549v1 Announce Type: new Abstract: Individual tree crown segmentation from aerial imagery underpins tree-level carbon accounting, biodiversity, and restoration monitoring at landscape sc...
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
Sep 14
EgoMaize: A First-Person Maize Instance Segmentation Benchmark under Severe Field Occlusion
arXiv:2609.12350v1 Announce Type: new Abstract: Close-range first-person field images are important for mobile maize phenotyping because many plant-level traits depend on in-canopy structures that ar...
arXiv Computer Vision
Sep 25
LeafTrackNet: A Deep Learning Framework for Robust Leaf Tracking in Top-Down Plant Phenotyping
LeafTrackNet is a deep learning framework that combines a YOLOv10-based leaf detector with a MobileNetV3-based embedding network to track individual leaves over time. The authors introduce CanolaTrack, a large benchmark dataset of 5,704 RGB images with 31,840 annotated leaf instances from 184 canola plants. When evaluated without prior fine‑tuning, LeafTrackNet outperforms existing methods on CanolaTrack, KOMATSUNA, and MSU‑PID datasets, achieving HOTA scores of 88.03, 87.33, and 74.20 respectively.
arXiv Machine Learning
Sep 17
MCLC-NET: Multimodal Continual Learning for Leaf Counting
The paper introduces MCLC‑NET, a multimodal continual learning framework for leaf counting that sequentially learns tasks using a memory buffer to retain key samples. It also presents MMLC, a new real‑world dataset containing RGB, depth, and thermal images across different crops and environmental conditions, organized in crop‑wise, time‑wise, and mixed orderings. Experiments show that MCLC‑NET outperforms existing methods on all three task orderings, achieving the lowest average mean squared errors.