A principled representation of elongated structures using heatmaps.

ABSTRACT: The detection of elongated structures like lines or edges is an essential component in semantic image analysis. Classical approaches that rely on significant image gradients quickly reach their limits when the structure is context-dependent, amorphous, or not directly visible. This study introduces a principled mathematical description of elongated structures with various origins and shapes. Among others, it serves as an expressive operational description of target functions that can be well approximated by Convolutional Neural Networks. The nominal position of a curve and its positional uncertainty are encoded as a heatmap by convolving the curve distribution with a filter function. We propose a low-error approximation to the expensive numerical integration by evaluating a distance-depend

SUBMITTER: Kordon F 

PROVIDER: S-EPMC10502041 | biostudies-literature | 2023 Sep

REPOSITORIES: biostudies-literature