Single-molecule biophysics has transformed our understanding of biology, but also of the
physics of life. More exotic than simple soft matter, biomatter lives far from thermal …

Stocking density presents a key factor affecting livestock and poultry production on a large
scale as well as animal welfare. However, the current manual counting method used in the …

Previous work generally believes that improving the spatial invariance of convolutional
networks is the key to object counting. However, after verifying several mainstream counting …

State-of-the-art methods for counting people in crowded scenes rely on deep networks to
estimate crowd density. They typically use the same filters over the whole image or over …

Endomicroscopy is an emerging imaging modality, that facilitates the acquisition of in vivo, in
situ optical biopsies, assisting diagnostic and potentially therapeutic interventions. While …

Class-agnostic object counting aims to count object instances of an arbitrary class at test
time. It is challenging but also enables many potential applications. Current methods require …

Existing counting methods often adopt regression-based approaches and cannot precisely
localize the target objects, which hinders the further analysis (eg, high-level understanding …

In this paper we address the problem of counting objects instances in images. Our models
are able to precisely estimate the number of vehicles in a traffic congestion, or to count the …

Cross-scene crowd counting is a challenging task where no laborious data annotation is
required for counting people in new target surveillance crowd scenes unseen in the training …

This paper concerns automated cell counting and detection in microscopy images. The
approach we take is to use convolutional neural networks (CNNs) to regress a cell spatial …