Hugging Face Trending Papers

OpenVeinNet: Robust Open-Set Finger Vein Verification with Dynamic Snake Convolution and Graph Learning

arXiv Computer Vision
Aug 27

OpenVeinNet: Robust Open-Set Finger Vein Verification with Dynamic Snake Convolution and Graph Learning

OpenVeinNet is a finger vein verification framework that tackles open-set scenarios by combining Dynamic Snake Convolution, which extracts local curvilinear vein structures through adaptive sampling, with a graph convolutional backbone that models long-range topological relationships between vein regions. The model introduces a Centroid Angular Hybrid Loss to promote intra-class compactness and inter-class angular separation in the embedding space. Experiments on five public datasets under leave-one-dataset-out training demonstrate strong cross-dataset generalisation, low equal error rates, and competitive true accept rates at fixed false accept rates.

By Sushrut Patwardhan, Raghavendra Ramachandra
arXiv Computer Vision
Aug 31

FVeinSyn: Synthetic Finger Vein Image Generator

FVeinSyn is a large‑scale synthetic finger‑vein image generator that separates vascular topology synthesis from imaging appearance rendering. It uses stochastic L‑systems to create anatomically valid, identity‑distinctive vein patterns, a cascaded region‑aware GAN to produce realistic near‑infrared images, and an intra‑class diversity generator to simulate realistic variations. The framework generated 500,000 images across 10,000 identities, and models trained with this data outperformed real‑data‑only baselines on eight public datasets, improving average accuracy by 27.43%.

By Yifan Wang, Jie Gui, Adams Wai Kin Kong, Baosheng Yu, Changsheng Chen, Qi Li, Zhenan Sun, James Tin-Yau Kwok, Alex Kot
Hugging Face Trending Papers
Aug 9

Topology-Aware Global-Local Mamba Networks for Palm Vein Biometrics

Palm-vein recognition is a fine-grained biometric task in which both local vascular texture and the global layout of the vessel tree carry discriminative information, while public datasets remain limited. We propose a topology-aware global-local backbone that combines multi-scale local features, a structureguided directional stream built on a fixed Sobel-magnitude edge prior, and a four-direction state-space scan global pathway within six Topology-Aware Blocks.

arXiv AI
6d ago

ReG-SAM: Reference Graph-Driven SAM for 2D Foundational Vessel Segmentation

ReG-SAM is a SAM-based framework designed for 2D vessel segmentation in medical images. It introduces reference graph prompt embeddings (GPEs) and vascular prototype embeddings (VPEs) to capture global spatial and fine-grained modality-specific vessel features, respectively. By building a modality-wise vascular database and learning these embeddings from reference masks, ReG-SAM consistently outperforms existing baselines across 19 datasets, especially on thin vessels.

By Donghang Lyu, Zichen Zhang, Oleh Dzyubachyk, Marius Staring
arXiv Machine Learning
Jul 27

CARDIAG: A Dense Segment Classification Benchmark of Deep Learning Architectures for Coronary Angiography

arXiv:2607. 22139v1 Announce Type: cross Abstract: Accurate pixel-level classification of coronary angiograms is critical for cardiovascular disease assessment, yet the field lacks standardized evaluation protocols.

By Dominik Bernard Lau, Hubert Malinowski, Jerzy Szyjut, Adam Brzeski, Tomasz Dziubich, Rados{\l}aw Targo\'nski, Tomasz Figatowski, Natalia Zieli\'nska