Matej Smíd

Learn More
In this paper we present a new approach for automated cell detection in single frames of 2D microscopic phase contrast images of cancer cells which is based on learning cellular texture features. The main challenge addressed in this paper is to deal with clusters of cells where each cell has a rather complex appearance composed of sub-regions with different(More)
In this paper we address the problem of recovering spatio-temporal trajectories of cancer cells in phase contrast video-microscopy where the user provides the paths on which the cells are moving. The paths are purely spatial, without temporal information. To recover the temporal information associated to a given path we propose an approach based on(More)
This paper describes a method for identifying and avoiding false stationary detections in background subtraction caused by movement of objects belonging to the background. This method is independent on used background subtraction algorithm. The proposed algorithm examines edges on contours of detected foreground objects to decide if it is a true detection(More)
In this paper we focus on tracking of players in team sports with multiple cameras. We evaluate a state of the art multi-camera multi-target tracking algorithm on a novel floorball dataset. We chose recent Probabilistic Occupancy Map and K-Shortest Path algorithms with a publicly available implementation that enables easy deployment. Both algorithms are(More)
Altered cell motility is considered to be a key factor in determining tumor invasion and metastasis. Epidermal growth factor (EGF) signaling has been implicated in this process by affecting cytoskeletal organization and dynamics in multiple ways. To sort the temporal and spatial regulation of EGF-dependent cytoskeletal re-organization in relation to a(More)
  • 1