Abstract: A method to compute a variable number of image planes, which are selected to better represent the scene while reducing the artifacts on produced novel views. This method analyses the structure of the scene by means of a depth map and selects the position in the Z-axis to split the original image into individual layers. The method also determines the number of layers in an adaptive way.

Abstract: Accurate peak detection in physiological signals is fundamental for several tasks related to health monitoring. A method for fine-tuning candidate peak positions and detecting peaks of interest in signals is provided. The fine-tuning method addresses the problem of low signal resolution and reduces the error with respect to the gold-standard reference signal usually collected at higher sampling frequencies. Obtaining accurate peak positions without modifying the sampling frequency is essential in the context of wearable devices, which often present limited computational resources and storage. Furthermore, the method enables selection of the peaks of interest by classifying their tuned positions according to a set of features extracted from morphological characteristics of the signal. The present pipeline is illustrated through inter-beat interval (IBI) estimation from wrist-PPG signals collected from smartwatches.

Abstract: A computer implemented method for classifying an input image for new view synthesis in a 3D visual effect, the input image being used to create an image representation comprising a set of target views based on the input 3D visual effect, each target view having an image size of H×W. The method includes computing an image score st for the set of target views based on a number of non-occluded pixels in the set of target views, wherein the image score indicates suitability of the input image for new view synthesis in a 3D visual effect. A system and a non-transitory computer readable storage medium for performing said method for classifying an input image for new view synthesis in a 3D visual effect.

Abstract: A method for predicting heart rate (HR) of a user during a physical activity, comprising: monitoring the user during the physical activity with an wearable electronic device comprising a photoplethysmography (PPG) sensor, an accelerometer, a memory and a processor; wherein the memory is configured to store user demographic data, PPG data and accelerometer data detected by the PPG sensor and the accelerometer, respectively. The method includes computing a PPG heart rate estimation and a PPG quality flag using the PPG data and accelerometer data, wherein the PPG quality flag indicates whether the PPG data is reliable or not; computing an exponential heart rate estimation by applying the PPG heart rate estimation, the PPG quality flag and the user's demographic data on an exponential approximation model; if the PPG quality flag is reliable, outputting the PPG heart rate estimation; otherwise, outputting the exponential heart rate estimation.

Abstract: The proposed invention aims at encoding contextual information for video analysis and understanding, by encoding spatial and temporal relationships of objects and the main agent in a scene. The main target application of the invention is human activity recognition. The encoding of such spatial and temporal relationships may be crucial to distinguish different categories of human activities and may be important to help in the discrimination of different video categories, aiming at video classification, retrieval, categorization and other video analysis applications.

Abstract: A method to compute a variable number of image planes, which are selected to better represent the scene while reducing the artifacts on produced novel views. This method analyses the structure of the scene by means of a depth map and selects the position in the Z-axis to split the original image into individual layers. The method also determines the number of layers in an adaptive way.

Abstract: The proposed invention aims at encoding contextual information for video analysis and understanding, by encoding spatial and temporal relationships of objects and the main agent in a scene. The main target application of the invention is human activity recognition. The encoding of such spatial and temporal relationships may be crucial to distinguish different categories of human activities and may be important to help in the discrimination of different video categories, aiming at video classification, retrieval, categorization and other video analysis applications.

Abstract: Method and system for displaying a digital human-like avatar in self-driving vehicles (SDV) including detecting a set of environmental data/characteristics in the vehicle surrounding area by sensors/cameras generating signals to be input for traditional machine learning classifiers on a computer vision module (CVM); receiving control system inputs from the sensors and processing actions to be performed; after receiving outputs from the control system, executing autonomous driving actions by an actuator system; and based on inputs from the CVM and a personalization module, combined with actions and car status information from the control system, generating a digital avatar performing human-like reactions/expressions/gestures to properly communicate/indicate the current and future actions of the SDV for external people on a SDV display device.

Abstract: The proposed method is used for classification in open-set scenarios, wherein often it is not possible to first obtain the training data for all possible classes that may arise during the testing stage. During the test phase, test samples belonging to one of the classes used in the training phase are classified based on a ratio between similarity scores, as known correct class and test samples belonging to any other class are to be rejected and classified as unknown.

Abstract: The proposed method is used for classification in open-set scenarios, wherein often it is not possible to first obtain the training data for all possible classes that may arise during the testing stage. During the test phase, test samples belonging to one of the classes used in the training phase are classified based on a ratio between similarity scores, as known correct class and test samples belonging to any other class are to be rejected and classified as unknown.