2D+t track detection via relative persistent homology

Autor: Abbas Rammal, Rabih Assaf, Alban Goupil, Valeriu Vrabie, Mohammad Kacim
Přispěvatelé: Université Saint-Esprit de Kaslik (USEK), Lebanese University [Beirut] (LU), Centre de Recherche en Sciences et Technologies de l'Information et de la Communication - EA 3804 (CRESTIC), Université de Reims Champagne-Ardenne (URCA)
Jazyk: angličtina
Rok vydání: 2021
Předmět:
Computer science
Computation
02 engineering and technology
Homology (mathematics)
relative homology
030218 nuclear medicine & medical imaging
03 medical and health sciences
algebraic topology
0302 clinical medicine
0202 electrical engineering
electronic engineering
information engineering
Electrical and Electronic Engineering
object tracking
Image domain
Persistent homology
business.industry
Pattern recognition
object detection
Object detection
persistent homology
Electronic
Optical and Magnetic Materials
Video tracking
[MATH.MATH-AT]Mathematics [math]/Algebraic Topology [math.AT]
Image sequence
020201 artificial intelligence & image processing
Computer Vision and Pattern Recognition
Artificial intelligence
business
[SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing
Software
Relative homology
Zdroj: International Journal of Imaging Systems and Technology
International Journal of Imaging Systems and Technology, Wiley, 2021, 31, pp.753-762. ⟨10.1002/ima.22503⟩
ISSN: 0899-9457
1098-1098
Popis: International audience; In this paper, we demonstrate that algebraic topology can be used to perform 2D+t object detection. After the construction of a topological complex for a 2D+t image sequence, we build a nested sequence of cell complexes on which relative persistent homology is computed. The relative homology adds to “absolute” homology the computation of classes related to the first and last frames of the sequence. By identifying 2D chains with large life spans, the most persistent classes are extracted. This allows for the identification of the interesting parts in a sequence and for the detection of the movement of objects despite continuous deformations in the image domain. The results obtained on a synthetic image and on two real biomedical images with moving vesicles recorded by a quantitative phase time‐lapse technique show the potential of this method. Comparing the method with a newly developed tracking tool proves that the strength of this method is its independence from prior parameters.
Databáze: OpenAIRE
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