Resolving Motion Correspondence for Densely Moving Points
IEEE Transactions on Pattern Analysis and Machine Intelligence
Digital Picture Processing
Computers and Intractability: A Guide to the Theory of NP-Completeness
Computers and Intractability: A Guide to the Theory of NP-Completeness
Image segmentation based on maximum-likelihood estimation and optimum entropy-distribution (MLE-OED)
Pattern Recognition Letters
A Noniterative Greedy Algorithm for Multiframe Point Correspondence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Online Tracking of Migrating and Proliferating Cells Imaged with Phase-Contrast Microscopy
CVPRW '06 Proceedings of the 2006 Conference on Computer Vision and Pattern Recognition Workshop
ACM Computing Surveys (CSUR)
Snakes, shapes, and gradient vector flow
IEEE Transactions on Image Processing
Level set analysis for leukocyte detection and tracking
IEEE Transactions on Image Processing
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In this paper, we present an automatic method for estimating the trajectories of Escherichia coli bacteria from in vivophase-contrast microscopy videos. To address the low-contrast boundaries in cellular images, an adaptive kernel-based technique is applied to detect cells in sequence of frames. Then a novel matching gain measure is introduced to cope with the challenges such as dramatic changes of cells' appearance and serious overlapping and occlusion. For multiple cell tracking, an optimal matching strategy is proposed to improve the handling of cell collision and broken trajectories. The results of successful tracking of Escherichia coli from various phase-contrast sequences are reported and compared with manually-determined trajectories, as well as those obtained from existing tracking methods. The stability of the algorithm with different parameter values is also analyzed and discussed.