SPADE: A Small Particle Detection Method Using A Dictionary Of Shapes Within The Marked Point Process Framework

International audienc

Using redundant information from multiple aerial images for the detection of bomb craters based on marked point processes

Many countries were the target of air strikes during World War II. Numerous unexploded bombs still exist in the ground. These duds can be tracked down with the help of bomb craters, indicating areas where unexploded bombs may be located. Such areas are documented in so-called impact maps based on detected bomb craters. In this paper, a stochastic approach based on marked point processes (MPPs) for the automatic detection of bomb craters in aerial images taken during World War II is presented. As most areas are covered by multiple images, the influence of redundant image information on the object detection result is investigated: We compare the results generated based on single images with those obtained by our new approach that combines the individual detection results of multiple images covering the same location. The object model for the bomb craters is represented by circles. Our MPP approach determines the most likely configuration of objects within the scene. The goal is reached by minimizing an energy function that describes the conformity with a predefined model by Reversible Jump Markov Chain Monte Carlo sampling in combination with simulated annealing. Afterwards, a probability map is generated from the automatic detections via kernel density estimation. By setting a threshold, areas around the detections are classified as contaminated or uncontaminated sites, respectively, which results in an impact map. Our results show a significant improvement with respect to its quality when redundant image information is used. © 2020 Copernicus GmbH. All rights reserved

Cells Detection Using Segmentation Competition

International audienceIn this paper we address the problem of cells detection from mi-croscopy images. We construct a dictionary of candidate shapes obtained from previous segmentation maps and define an energy function to select the best candidates. The energy minimization is performed by an iterative graph cut algorithm. The proposed approach optimally combines the segmentation maps obtained with different methods and/or parameters. We show on synthetic and real data that this process allows to drastically improve the performance of each individual segmentation