Membrane dynamics
Assessing the dynamics of plasma membrane events in live cell fluorescence microscopy is of paramount interest to understand cell mechanisms. In collaboration with UMR144, we develop methods to detect vesicle fusion events, and then estimate the associated dynamics in image sequences of total internal reflection fluorescence microscopy (TIRFM). Various dynamic models (including translation, diffusion and dissociation) are tested to classify the dynamics of each detected event.
Spot detection
To quantitatively analyze dynamic phenomena such as the aforementioned membrane dynamics, subcellular particles of interest have to be accurately detected. In 2014, we proposed ATLAS, a method enabling the segmentation of vesicles in fluorescence microscopy images. The segmentation stage amounts to thresholding the Laplacian of Gaussian (LoG) of the image. The optimal LoG scale is automatically selected in a scale-space framework, and the threshold is locally deduced from a user-specified probability of false alarm.
Crowd motion analysis
Assessing crowd behaviors from videos is a difficult task while of interest in many applications. We have defined a novel approach, which identifies, from two successive frames only, crowd behaviors expressed by simple image motion patterns. It relies on the estimation of a collection of sub-affine motion models in the image, a local motion classification based on a penalized likelihood criterion, and a regularization stage involving inhibition and reinforcement factors. Relying on this motion descriptor, we have also developed an original and simple method for recovering the dominant paths followed by people in the observed scene. We are now working on the on-line detection and localization of abnormal behaviors in videos of crowded scenes.