Abstract: Methods, systems and apparatuses may provide for technology that transforms input data into a wave format, wherein the wave format includes a plurality of sinusoids for one or more data points in the input data, converts each sinusoid into a binary sequence to obtain a plurality of binary sequences for each of the one or more data points in the input data, and submits the plurality of binary sequences to a machine learning model.

Abstract: A method for video compression through image processing and object detection, based on images or a digital video stream of images, to enhance and isolate frequency domain signals representing content to be identified, and decrease or ignore frequency domain noise with respect to the content. A digital image or sequence of digital images defined in a spatial domain are obtained. One or more pairs of sparse zones are selected, each pair generating a selected feature, each zone defined by two sequences of spatial data. The selected features are transformed into frequency domain data. The transfer function, shape and direction of the frequency domain data are varied for each zone, thus generating a normalized complex vector for each feature. The normalized complex vectors are then combined to define a model of the content to be identified.

Abstract: A method for content detection based on images or a digital video stream of images, to enhance and isolate frequency domain signals representing content to be identified, and decrease or ignore frequency domain noise with respect to the content. A digital image or sequence of digital images defined in a spatial domain are obtained. One or more pairs of sparse zones are selected, each pair generating a feature, each zone defined by two sequences of spatial data. The selected features are transformed into frequency domain data. The transfer function, shape and direction of the frequency domain data are varied for each zone, thus generating a normalized complex vector for each feature. The normalized complex vectors are then combined to define a model of the content to be identified.

Abstract: A method for video compression through image processing and object detection, to be carried out by an electronic processing unit, based either on images or on a digital video stream of images, the images being defined by a single frame or by sequences of frames of said video stream, with the aim of enhancing and then isolating the frequency domain signals representing a content to be identified, and decreasing or ignoring the frequency domain noise with respect to the content within the images or the video stream, comprises the steps of: obtaining a digital image or a sequence of digital images from either a corresponding single frame or a corresponding sequence of frames of said video stream, all the digital images being defined in a spatial domain; selecting one or more pairs of sparse zones, each covering at least a portion of said single frame or at least two frames of said sequence of frames, each pair of sparse zones generating a selected feature, each zone being defined by two sequences of spatial data

Abstract: A method for content detection to be carried out by an electronic processing unit, based either on images or on a digital video stream of images, the images being defined by a single frame or by sequences of frames of said video stream, with the aim of enhancing and then isolating the frequency domain signals representing a content to be identified, and decreasing or ignoring the frequency domain noise with respect to the content within the images or the video stream, comprising the steps of: obtaining a digital image or a sequence of digital images from either a corresponding single frame or a corresponding sequence of frames of said video stream, all the digital images being defined in a spatial domain; selecting one or more pairs of sparse zones, each covering at least a portion of said single frame or at least two frames of said sequence of frames, each pair of sparse zones generating a feature, each zone being defined by two sequences of spatial data; transforming the selected features into frequency dom

Abstract: A method for gaze tracking achieves high performances at the same time requiring both limited processor engagement and reduced power, so as to be particularly but not exclusively fit for mobile uses is described.

Abstract: A method for gaze tracking achieves high performances at the same time requiring both limited processor engagement and reduced power, so as to be particularly but not exclusively fit for mobile uses is described.

Abstract: A method for gaze tracking achieves high performances at the same time requiring both limited processor engagement and reduced power, so as to be particularly but not exclusively fit for mobile uses is described.