Detecting spot-like objects of different sizes in images is needed in many applications. Multiple image scales must then be handled for reliable spot segmentation.
We define an original criterion based on the a contrario approach and the LoG scale-space framework to automatically select the meaningful scales.
We then design a coarse-to-fine multi-scale spot segmentation scheme involving
a locally adaptive thresholding across scales, to come up with the final map of segmented spots.
We report experimental results on simulated and real images of different types, and we demonstrate that our method outperforms other existing methods.