IEEE Transactions on Pattern Analysis and Machine Intelligence
Target perceivability and its applications
IEEE Transactions on Signal Processing
IEEE Transactions on Image Processing
Particle tracking in fluorescent microscopy images improved by morphological source separation
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
Multiple hypothesis tracking in cluttered condition
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
Multi-target tracking of packed yeast cells
ISBI'10 Proceedings of the 2010 IEEE international conference on Biomedical imaging: from nano to Macro
MICCAI'12 Proceedings of the 15th international conference on Medical Image Computing and Computer-Assisted Intervention - Volume Part I
Semantic world modeling using probabilistic multiple hypothesis anchoring
Robotics and Autonomous Systems
A multiple hypothesis based method for particle tracking and its extension for cell segmentation
IPMI'13 Proceedings of the 23rd international conference on Information Processing in Medical Imaging
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Multiple hypothesis tracking (MHT) is a preferred technique for solving the data association problem in modern multiple target tracking systems. However in bioimaging applications, its use has long been thought impossible due to the prohibitive cost induced by the high number of objects that need to be tracked and the poor quality of images. We show in this paper that this broadly accepted view should change. We propose a MHT algorithm (fMHT) that is fast even when dealing with very noisy images of very numerous targets. We have applied the method to the analysis of two sets of real microscopy images that contain thousands of biological targets. By doing so we prove the benefits of the approach when tracking in very noisy environments such as low-light level fluorescent microscopy images.