22 papers · ranked by Valyu relevance
Liang, Peixian, Ding, Yifan + 20 more
— State-of-the-art (SOTA) methods for cell instance segmentation are based on deep learning (DL) semantic segmentation approaches, focusing on distinguishing foreground pixels from background pixels. In order to identify cell instances from foreground pixels (e.g., pixel clustering), most methods decompose instance…
Weidong Zhu, Piao Wang, Kuifeng Luan, Xiaohao Cai + 2 more
Ship instance segmentation in remote sensing images is essential for maritime applications such as intelligent surveillance and port management. However, this task remains challenging due to dense target distributions, large variations in ship scales and shapes, and limited high-quality datasets. The existing YOLOv8…
Youqing Chen, Hang Liu, Lun Wang, Chen Chen + 7 more
Infrared images of caged chickens can provide valuable insights into their health status. Accurately detecting and segmenting individual chickens in these images is essential for effective health monitoring in large-scale chicken farming. However, the presence of obstacles such as cages, feeders, and drinkers can…
Diego Martí-Pérez, Valery Naranjo, Adrián Colomer, Ebrahim Karami
Cell segmentation plays a key role in a wide range of biomedical imaging applications, from single-cell analysis to pathology assessment. While classical deep learning architectures such as U-Net, StarDist, and HoVer-Net have set strong baselines, their reliance on domain-specific training limits generalization across…
Yuqian Yuan, Wentong Li, Zhaocheng Li, Yutong Lin + 5 more
In this paper, we introduce InstructSAM, a unified and streamlined framework designed for multi-instance segmentation under arbitrary instructions. We formulates instruction-driven instance segmentation as a set-structured query prediction problem and propose an explicit reasoning-to-instance query interface that…
Leon Sick, Lukas Hoyer, Dominik Engel, Pedro Hermosilla + 1 more
In recent years, the state-of-the-art in unsupervised video instance segmentation has heavily relied on synthetic video data, generated from object-centric image datasets such as ImageNet. However, video synthesis by artificially shifting and scaling image instance masks fails to accurately model realistic motion in…
Sreenivas Bhattiprolu, Manita Toor, Sebastian Soyer
Modern biological imaging generates large, complex datasets that require scalable and reproducible image analysis methods. Deep learning has demonstrated strong performance on bioimage segmentation tasks, but training custom models has remained inaccessible to many researchers due to requirements for GPU…
Tiyao Zhang, Xue Yuan, Hongze Xu, Toon Goedemé
Accurate segmentation of surgical instruments in endoscopic videos is crucial for robot-assisted surgery and intraoperative analysis. This paper presents a Segment-then-Classify framework that decouples mask generation from semantic classification to enhance spatial completeness and temporal stability. First, a…
Xingyu Chen, Jiasai Wu, Junjie Hou, Xiao Liu + 2 more
Traditional visual Simultaneous Localization and Mapping (SLAM) systems achieve high accuracy in static environments. However, in indoor dynamic scenes with frequent object motions, the presence of moving objects severely violates the scene rigidity assumption, often leading to significant performance degradation and…
Peng Liu, Boyu Shen, Liyuan Liu, Qiong Wang + 5 more
We present MitoEM 2.0, a curated data resource for training and evaluating three-dimensional (3D) mitochondria instance segmentation in volume electron microscopy. The collection assembles multiscale vEM datasets (FIB-SEM, SBF-SEM, ssSEM) spanning diverse tissues and species, with expert-verified instance labels…
Kaden Stillwagon, Alexandra D. VandeLoo, Craig R. Forest
Reliable evaluation of instance segmentation models requires metrics that accurately and consistently reflect segmentation quality. However, the metrics most widely used in biological imaging carry fundamental mathematical weaknesses: hard Intersection-over-Union (IoU) thresholds that produce discontinuous, low…
Abrar Rahman Abir, Anik Saha, Ruwad Naswan, Md. Shamsuzzoha Bayzid
Accurate reconstruction of neural circuits from electron microscopy (EM) data is central to connectomics, yet modern datasets are now so large and heterogeneous that manual annotation and dataset-specific model retraining have become major challenges. While recent EM foundation models provide general visual…
Longfei Qie, Chunlei Chai, Ruixue Wang, Chao Bi + 4 more
Highlights What are the main findings?1. We present TrackRefine, a plug-and-play, decoupled framework that enhances online multi-object tracking and segmentation without requiring modifications to the front-end instance segmenter or additional end-to-end joint training. 2. Improving mask quality, memory reliability…
Katja Kossira, Yunxuan Zhu, Jürgen Seiler, André Kaup
©2025 IEEE. Published in 2025 International Conference on Visual Communications and Image Processing (VCIP), scheduled for 01-04 December 2025 in Klagenfurt, Austria. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for…
Tridib K. Biswas, Jonathan Vacher, Sophie Molholm, Pascal Mamassian + 1 more
The visual system operates by segmenting visual inputs into distinct perceptual objects. Segmentation is dynamic, as revealed by the tempo of perceptual choices and neural activity in visual cortex. Dynamics for natural stimuli however, are poorly understood because natural scene segmentation is ambiguous and…
Tomasz Przygodzki
Quantitative analysis of microscopic images has become a standard in basic biological and biomedical research. Deep machine learning provided a powerful tool facilitating this process. However, practical adoption of deep machine learning to image analysis may be difficult for a researcher who lacks basic coding skills.…
Kaustubh Shivshankar Shejole, Gaurav Mishra
—Interactive graph-based segmentation methods partition an image into foreground and background regions with the aid of user inputs. However, existing approaches often suffer from high computational costs, sensitivity to user interactions, and degraded performance when the foreground and background share similar color…
Christian Hallgrimson, Y. Lydia Li, Claire A. Shou, Ben Cardoen + 5 more
Single-molecule localization microscopy (SMLM) achieves nanoscale imaging of complex protein structures in the cell. However, the ability to capture structural variability across cell conditions (cell lines, gene expression, treatment) from 3D point cloud SMLM data remains limited. We present siMILe, a…
Yichen He, Eleftherios Ioannou, Kathryn Harris, Gavin Thomas + 3 more
Fine-grained localisation of plumage regions is a prerequisite for computational analyses of avian colouration, patterning and visual traits in ecological and evolutionary research. Progress is limited by the scarcity of image resources with annotations aligned to biologically meaningful anatomical units: existing…
Yuchen Guo, Junli Gong, Hongmin Cai, Yiu-ming Cheung + 1 more
Recent segmentation models couple large language models (LLMs) with mask decoders to ground complex language expressions into masks, yet their instructions remain target-referential: they describe, constrain, or imply the region to be segmented. However, in real-world embodied interaction, human instructions are often…
Authors not listed
Ni-rich cathode materials suffer from structural instability when cycled to high cutoff voltages. A transformation from the layered crystal structure to other phases, such as spinel and/or rocksalt, deteriorates the particle surface at low states of lithiation. Inhomogeneous potential and concentration fields as they…
Authors not listed
Machine olfaction—the artificial replication of the sense of smell—faces significant challenges due to the absence of large, standardized training datasets. Unlike vision, language, and audio models, which benefit from extensive corpora such as ImageNet, GLUE, and AudioSet, olfaction lacks scaled equivalents and…