Individuating and locating repetitive patterns in still images is a fundamental task in image processing, typically achieved by means of correlation strategies. In this paper, we provide a solid solution to this task using a differential geometry approach, operating on Lie algebra, and exploiting a mixture of templates. The proposed method asks the user to locate a few instances of the target patterns (seeds) that become visual templates used to explore the image. We propose an iterative algorithm to locate patches similar to the seeds working in three steps: first, clustering the detected patches to generate templates of different classes, then looking for the affine transformations, living on a Lie algebra that best links the templates and the detected patches, and finally detecting new patches with a convolutional strategy. The process ends when no new patches are found. We will show how our method is able to process heterogeneous unstructured images with multiple visual motifs and extremely crowded scenarios with high precision and recall, outperforming all the state-of-the-art methods.

The topic of crowd modeling in computer vision usually assumes a single generic typology of crowd, which is very simplistic. In this paper we adopt a taxonomy that is widely accepted in sociology, focusing on a particular category, the spectator crowd, which is formed by people “interested in watching something specific that they came to see” [6]. This can be found at the stadiums, amphitheaters, cinema, etc. In particular, we propose a novel dataset, the Spectators Hockey (S-HOCK), which deals with 4 hockey matches during an international tournament. In the dataset, a massive annotation has been carried out, focusing on the spectators at different levels of details: at a higher level, people have been labeled depending on the team they are supporting and the fact that they know the people close to them; going to the lower levels, standard pose information has been considered (regarding the head, the body) but also fine grained actions such as hands on hips, clapping hands etc. The labeling focused on the game field also, permitting to relate what is going on in the match with the crowd behavior. This brought to more than 100 millions of annotations, useful for standard applications as people counting and head pose estimation but also for novel tasks as spectator categorization. For all of these we provide protocols and baseline results, encouraging further research.

Early detection of epileptic seizures is still a challenge in the state-of-the-art. The proposed method exploits multiresolution sample entropy for both seizure detection and fingerprinting. First, a SVM classifier is used to detect the seizures' onset with high temporal accuracy, then the seizures fingerprints across the subband structure are derived exploiting sample entropy non stationarity. Over 8 hours of EEG data recordings from patients suffering from temporal lobe epilepsy were used for training and testing the system, and validation was performed based on annotation by one expert neurophysiologist. All the seizures were successfully detected and provides an effective time-scale fingerprinting of their evolution. A prominent impact in high (γ) frequency band was observed whose neurophysiological ground is currently under investigation.