MedianStruck for long-term tracking applications

Baumann, Florian; Dayangac, Enes; Aulinas, Josep; Zobel, Matthias

doi:10.1109/ipta.2016.7821029

Cited by 1 publication

(1 citation statement)

References 16 publications

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“…Object detection and tracking are usually regarded as major and challenging tasks in the pipeline of image processing and pattern analysis. There are many techniques that have been proposed and developed in the literature, such as frame differencing, [1][2][3] point detectors, [4][5][6] background subtraction, 7,8 supervised learning, [9][10][11] and deep learning techniques [12][13][14] ; see References 15,16 for thorough explanations. Most of the existing techniques are nongeneric methods based mainly on complex algorithms controlled by many parameters, constraints and metrics.…”

Section: Introductionmentioning

confidence: 99%

2D+t track detection via relative persistent homology

Assaf

Goupil

Rammal

et al. 2020

Int J Imaging Syst Tech

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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.

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