TY - GEN

T1 - Understanding what we cannot see

T2 - International Seminar on Video Processing and Computational Video

AU - Leal-Taixé, Laura

AU - Heydt, Matthias

AU - Rosenhahn, Axel

AU - Rosenhahn, Bodo

PY - 2011

Y1 - 2011

N2 - Digital in-line holography is a microscopy technique which got an increasing attention over the last few years in the fields of microbiology, medicine and physics, as it provides an efficient way of measuring 3D microscopic data over time. In this paper, we present a complete system for the automatic analysis of digital in-line holographic data; we detect the 3D positions of the microorganisms, compute their trajectories over time and finally classify these trajectories according to their motion patterns. Tracking is performed using a robust method which evolves from the Hungarian bipartite weighted graph matching algorithm and allows us to deal with newly entering and leaving particles and compensate for missing data and outliers. In order to fully understand the behavior of the microorganisms, we make use of Hidden Markov Models (HMMs) to classify four different motion patterns of a microorganism and to separate multiple patterns occurring within a trajectory. We present a complete set of experiments which show that our tracking method has an accuracy between 76% and 91%, compared to ground truth data. The obtained classification rates on four full sequences (2500 frames) range between 83.5% and 100%.

AB - Digital in-line holography is a microscopy technique which got an increasing attention over the last few years in the fields of microbiology, medicine and physics, as it provides an efficient way of measuring 3D microscopic data over time. In this paper, we present a complete system for the automatic analysis of digital in-line holographic data; we detect the 3D positions of the microorganisms, compute their trajectories over time and finally classify these trajectories according to their motion patterns. Tracking is performed using a robust method which evolves from the Hungarian bipartite weighted graph matching algorithm and allows us to deal with newly entering and leaving particles and compensate for missing data and outliers. In order to fully understand the behavior of the microorganisms, we make use of Hidden Markov Models (HMMs) to classify four different motion patterns of a microorganism and to separate multiple patterns occurring within a trajectory. We present a complete set of experiments which show that our tracking method has an accuracy between 76% and 91%, compared to ground truth data. The obtained classification rates on four full sequences (2500 frames) range between 83.5% and 100%.

KW - digital in-line holographic microscopy

KW - graph matching

KW - Hidden Markov Models

KW - motion pattern classification

KW - multi-level Hungarian

KW - particle tracking

UR - http://www.scopus.com/inward/record.url?scp=81855226739&partnerID=8YFLogxK

U2 - 10.1007/978-3-642-24870-2_3

DO - 10.1007/978-3-642-24870-2_3

M3 - Conference contribution

AN - SCOPUS:81855226739

SN - 9783642248696

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 52

EP - 76

BT - Video Processing and Computational Video - International Seminar, Revised Papers

Y2 - 10 October 2010 through 15 October 2010

ER -