- Tytuł:
- A simultaneous localization and tracking method for a worm tracking system
- Autorzy:
-
Kowalski, M.
Kaczmarek, P.
Kabaciński, R.
Matuszczak, M.
Tranbowicz, K.
Sobkowiak, R. - Powiązania:
- https://bibliotekanauki.pl/articles/330526.pdf
- Data publikacji:
- 2014
- Wydawca:
- Uniwersytet Zielonogórski. Oficyna Wydawnicza
- Tematy:
-
Caenorhabditis elegans behavior
worm tracking
computer vision
image processing
feature extraction
wizja komputerowa
przetwarzanie obrazu
ekstrakcja cech - Opis:
- The idea of worm tracking refers to the path analysis of Caenorhabditis elegans nematodes and is an important tool in neurobiology which helps to describe their behavior. Knowledge about nematode behavior can be applied as a model to study the physiological addiction process or other nervous system processes in animals and humans. Tracking is performed by using a special manipulator positioning a microscope with a camera over a dish with an observed individual. In the paper, the accuracy of a nematode’s trajectory reconstruction is investigated. Special attention is paid to analyzing errors that occurred during the microscope displacements. Two sources of errors in the trajectory reconstruction are shown. One is due to the difficulty in accurately measuring the microscope shift, the other is due to a nematode displacement during the microscope movement. A new method that increases path reconstruction accuracy based only on the registered sequence of images is proposed. The method Simultaneously Localizes And Tracks (SLAT) the nematodes, and is robust to the positioning system displacement errors. The proposed method predicts the nematode position by using NonParametric Regression (NPR). In addition, two other methods of the SLAT problem are implemented to evaluate the NPR method. The first consists in ignoring the nematode displacement during microscope movement, and the second is based on a Kalman filter. The results suggest that the SLAT method based on nonparametric regression gives the most promising results and decreases the error of trajectory reconstruction by 25% compared with reconstruction based on data from the positioning system.
- Źródło:
-
International Journal of Applied Mathematics and Computer Science; 2014, 24, 3; 599-609
1641-876X
2083-8492 - Pojawia się w:
- International Journal of Applied Mathematics and Computer Science
- Dostawca treści:
- Biblioteka Nauki
Artykuł