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Multiple Hypothesis Hypergraph Tracking for Posture Identification in Embryonic Caenorhabditis elegans [article]

Andrew Lauziere, Evan Ardiel, Stephen Xu, Hari Shroff
2022 arXiv   pre-print
Adversarial conditions such as volatile object motion and imperfect detections create a challenging tracking landscape in which established methods may yield inadequate results.  ...  The method extends traditional multiple hypothesis tracking (MHT) via hypergraphs to model correlated object motion, allowing for robust tracking in challenging scenarios.  ...  This work used the computational resources of the NIH HPC Biowulf cluster. (http://hpc.nih.gov). We thank Mr. Brandon Harvey for providing annotation data and Dr.  ... 
arXiv:2111.06425v2 fatcat:2fm3hdpoq5bfzbjw4wksubxobm

Pedestrian Models for Autonomous Driving Part I: Low-Level Models, From Sensing to Tracking

Fanta Camara, Nicola Bellotto, Serhan Cosar, Dimitris Nathanael, Matthias Althoff, Jingyuan Wu, Johannes Ruenz, Andre Dietrich, Charles Fox
2020 IEEE transactions on intelligent transportation systems (Print)  
Planning AV actions in the presence of pedestrians thus requires modelling of their probable future behaviour as well as detecting and tracking them.  ...  Technologies at these levels are found to be mature and available as foundations for use in high-level systems, such as behaviour modelling, prediction and interaction control.  ...  It is likely, in the future, that neural network approaches will come to dominate this area as with detection.  ... 
doi:10.1109/tits.2020.3006768 fatcat:awa5dgk4rbazteetyyqrndbgxq

Content-aware frame interpolation (CAFI): Deep Learning-based temporal super-resolution for fast bioimaging [article]

Martin Priessner, David C.A. Gaboriau, Arlo Sheridan, Tchern Lenn, Jonathan R. Chubb, Uri Manor, Ramon Vilar Compte, Romain F. Laine
2021 bioRxiv   pre-print
recurrent neural networks, that are highly suited for accurately predicting images in between image pairs, therefore improving the temporal resolution of image series as a post-acquisition analysis step  ...  We demonstrate its capabilities for single-particle tracking methods applied to the study of lysosome trafficking.  ...  recurrent neural networks, that are highly suited for accurately predicting images in between image pairs, therefore improving the temporal resolution of image series as a post-acquisition analysis step  ... 
doi:10.1101/2021.11.02.466664 fatcat:xyudfvgetbcbxo3ww5564rur5y

Three-Dimensional Single Particle Tracking and Its Applications in Confined Environments

Yaning Zhong, Gufeng Wang
2020 Annual Review of Analytical Chemistry  
Single particle tracking (SPT) has proven to be a powerful technique in studying molecular dynamics in complicated systems.  ...  Especially of interest are those based on point spread function engineering, which are simple in instrumentation and can be easily adapted and used in analytical labs.  ...  In the regression process, these multiple hidden layers bearing information about special features of the image at multiple levels are fully connected, resembling how a neural network works.  ... 
doi:10.1146/annurev-anchem-091819-100409 pmid:32097571 fatcat:tk2lnsenivahvckgpdi5jcyki4

Pedestrian Models for Autonomous Driving Part I: low level models, from sensing to tracking [article]

Fanta Camara, Nicola Bellotto, Serhan Cosar, Dimitris Nathanael, Matthias Althoff, Jingyuan Wu, Johannes Ruenz, André Dietrich, Charles W. Fox
2020 arXiv   pre-print
Planning AV actions in the presence of pedestrians thus requires modelling of their probable future behaviour as well as detection and tracking which enable such modelling.  ...  Technologies at these levels are found to be mature and available as foundations for use in higher level systems such as behaviour modelling, prediction and interaction control.  ...  In [148], Milan et al. proposed a complete online multiple people tracking method based on recurrent neural networks. c) MTT Under Multiple Cameras: Also called Multi-Target Multi-Camera (MTMC), this type  ... 
arXiv:2002.11669v1 fatcat:fgg5j5jdwrbujjgtj2uhgrx2am

2020 Index IEEE Transactions on Image Processing Vol. 29

2020 IEEE Transactions on Image Processing  
., +, TIP 2020 6561-6573 A Recurrent Neural Network for Particle Tracking in Microscopy Images Using Future Information, Track Hypotheses, and Multiple Detections.  ...  Chen, G., +, TIP 2020 5877-5888 Fluorescence A Recurrent Neural Network for Particle Tracking in Microscopy Images Using Future Information, Track Hypotheses, and Multiple Detections.  ... 
doi:10.1109/tip.2020.3046056 fatcat:24m6k2elprf2nfmucbjzhvzk3m

Table of contents

2020 IEEE Transactions on Image Processing  
Shao 3665 A Recurrent Neural Network for Particle Tracking in Microscopy Images Using Future Information, Track Hypotheses, and Multiple Detections .....................................................  ...  Gorthi, and S. Gorthi 4862 ICNet: Information Conversion Network for RGB-D Based Salient Object Detection .... G.Li, Z. Liu, and H.  ... 
doi:10.1109/tip.2019.2940372 fatcat:h23ul2rqazbstcho46uv3lunku

Accelerating the experimental responses on cell behaviors: a long-term prediction of cell trajectories using Social Generative Adversarial Network

Maria Colomba Comes, J. Filippi, A. Mencattini, F. Corsi, P. Casti, A. De Ninno, D. Di Giuseppe, M. D'Orazio, L. Ghibelli, F. Mattei, G. Schiavoni, L. Businaro (+2 others)
2020 Scientific Reports  
However, the amount of information stored in long-time experiments may constitute a serious bottleneck of the experimental pipeline.  ...  The incremented uptake provided by time-lapse microscopy in Organ-on-a-Chip (OoC) devices allowed increased attention to the dynamics of the co-cultured systems.  ...  Vacchelli, A. Sauvat and G. Cerrato (Centre de Recherche des Cordeliers de Jussieu, Paris) for providing experimental videos on OoC devices used in the data analysis for this paper.  ... 
doi:10.1038/s41598-020-72605-3 pmid:32973301 fatcat:irf6jxsyevfaxmcyemawm7s2su

Deep learning models for lipid-nanoparticle-based drug delivery [article]

Philip John Harrison, Håkan Wieslander, Alan Sabirsh, Johan Karlsson, Victor Malmsjö, Andreas Hellander, Carolina Wählby, Ola Spjuth
2020 bioRxiv   pre-print
In the first approach we use a convolutional neural network extracting per-cell features at each time point.  ...  Large-scale time-lapse microscopy experiments are useful to understand delivery and expression in RNA-based therapeutics.  ...  Thanks go also to Marco Capuccini, Alex Danis, Anindya Gupta, Ankit Gupta, Anders Larsson, Thomas Schön, Oliver Stein, Robin Strand and Niklas Wahlström for their help and advice.  ... 
doi:10.1101/2020.04.06.027672 fatcat:gmuaa5sx4jg4jko657rsqlz3li

MPM: Joint Representation of Motion and Position Map for Cell Tracking [article]

Junya Hayashida and Kazuya Nishimura and Ryoma Bise
2020 arXiv   pre-print
Conventional cell tracking methods detect multiple cells in each frame (detection) and then associate the detection results in successive time-frames (association).  ...  It guarantees coherence such that if a cell is detected, the corresponding motion flow can always be obtained. It is a simple but powerful method for multi-object tracking in dense environments.  ...  Acknowledgments This work was supported by JSPS KAKENHI Grant Numbers JP18H05104 and JP18H04738.  ... 
arXiv:2002.10749v2 fatcat:t3vghoqykzakfchdawgyoe5qqa

MPM: Joint Representation of Motion and Position Map for Cell Tracking

Junya Hayashida, Kazuya Nishimura, Ryoma Bise
2020 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)  
Conventional cell tracking methods detect multiple cells in each frame (detection) and then associate the detection results in successive time-frames (association).  ...  It guarantees coherence such that if a cell is detected, the corresponding motion flow can always be obtained. It is a simple but powerful method for multi-object tracking in dense environments.  ...  Acknowledgments This work was supported by JSPS KAKENHI Grant Numbers JP18H05104 and JP18H04738.  ... 
doi:10.1109/cvpr42600.2020.00388 dblp:conf/cvpr/HayashidaNB20 fatcat:7cwnakr3nvhspa2x5uzyxcjyfy

Applications and Techniques for Fast Machine Learning in Science [article]

Allison McCarn Deiana, Joshua Agar, Michaela Blott, Giuseppe Di Guglielmo, Javier Duarte, Philip Harris, Scott Hauck, Mia Liu, Mark S. Neubauer, Jennifer Ngadiuba, Seda Ogrenci-Memik, Maurizio Pierini (+74 others)
2021 arXiv   pre-print
The material for the report builds on two workshops held by the Fast ML for Science community and covers three main areas: applications for fast ML across a number of scientific domains; techniques for  ...  In this community review report, we discuss applications and techniques for fast machine learning (ML) in science -- the concept of integrating power ML methods into the real-time experimental data processing  ...  Besides, recurrent neural networks (RNNs) based autoencoders have been explored to detect gravitational wave using an unsupervised strategy [188] .  ... 
arXiv:2110.13041v1 fatcat:cvbo2hmfgfcuxi7abezypw2qrm

Learning Neural Textual Representations for Citation Recommendation

Binh Thanh Kieu, Inigo Jauregi Unanue, Son Bao Pham, Hieu Xuan Phan, Massimo Piccardi
2021 2020 25th International Conference on Pattern Recognition (ICPR)  
Rajkumar; Ladi, Anna; Bauckhage, Christian 1860 Tackling Contradiction Detection in German Using Machine Translation and End-to-End Recurrent Neural Networks DAY 1 -Jan 12, 2021 Shen, Xi; Pastrolin  ...  V. 2622 Improving Word Recognition using Multiple Hypotheses and Deep Embeddings DAY 3 -Jan 14, 2021 Li, Xiaoqian; Liu, jie; Zhang, Guixuan; Zhang, shuwu 2627 IBN-STR: A Robust Text Recognizer  ... 
doi:10.1109/icpr48806.2021.9412725 fatcat:3vge2tpd2zf7jcv5btcixnaikm

Applications and Techniques for Fast Machine Learning in Science

Allison McCarn Deiana, Nhan Tran, Joshua Agar, Michaela Blott, Giuseppe Di Guglielmo, Javier Duarte, Philip Harris, Scott Hauck, Mia Liu, Mark S. Neubauer, Jennifer Ngadiuba, Seda Ogrenci-Memik (+35 others)
2022 Frontiers in Big Data  
The material for the report builds on two workshops held by the Fast ML for Science community and covers three main areas: applications for fast ML across a number of scientific domains; techniques for  ...  In this community review report, we discuss applications and techniques for fast machine learning (ML) in science—the concept of integrating powerful ML methods into the real-time experimental data processing  ...  Frame-Based Images Frame-based images are a suitable representation of the experimental data in multiple domains such as neutrino detection with time projection chambers in particle physics.  ... 
doi:10.3389/fdata.2022.787421 pmid:35496379 pmcid:PMC9041419 fatcat:5w2exf7vvrfvnhln7nj5uppjga

Blood vessel segmentation algorithms — Review of methods, datasets and evaluation metrics

Sara Moccia, Elena De Momi, Sara El Hadji, Leonardo S. Mattos
2018 Computer Methods and Programs in Biomedicine  
Blood vessel segmentation is a topic of high interest in medical image analysis since the analysis of vessels is crucial for diagnosis, treatment planning and execution, and evaluation of clinical outcomes  ...  For each analyzed approach, summary tables are presented reporting imaging technique used, anatomical region and performance measures employed.  ...  Neural networks are used in [64] to segment retinal vessels.  ... 
doi:10.1016/j.cmpb.2018.02.001 pmid:29544791 fatcat:cchvmvuy5zgzzetv5hwc67nnbe
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