Keeping count: Leveraging temporal context to count heavily overlapping objects

By Luca Fiaschi, Gregor Konstantin, Bruno Afonso, Marta Zlatic, Fred A. Hamprecht. 2013

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How researchers see this paper

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Finely-grained annotated datasets for image-based plant phenotyping (2016, methodology): “Learning-based counting techniques could help here (Lempitsky and Zisserman, 2010; Fiaschi et al., 2013).”
Annotated Image Datasets of Rosette Plants (2014, methodology): “We also do hope that this dataset and its future versions, would be used from the computer vision community to learn suitable image statistics [19], adapt and test counting algorithms with [20] and without temporal information [21, 22, 23], segmentation algorithms [24, 25, 26, 27, 28], multi-label s”