Abstract: An imaging device is presented for use in an imaging system capable of improving the image quality. The imaging device has one or more optical systems defining an effective aperture of the imaging device. The imaging device comprises a lens system having an algebraic representation matrix of a diagonalized form defining a first Condition Number, and a phase encoder utility adapted to effect a second Condition Number of an algebraic representation matrix of the imaging device, smaller than said first Condition Number of the lens system.

Abstract: An imaging device is presented for use in an imaging system capable of improving the image quality. The imaging device has one or more optical systems defining an effective aperture of the imaging device. The imaging device comprises a lens system having an algebraic representation matrix of a diagonalized form defining a first Condition Number, and a phase encoder utility adapted to effect a second Condition Number of an algebraic representation matrix of the imaging device, smaller than said first Condition Number of the lens system.

Abstract: A method of identifying a person, the method comprising: acquiring spatiotemporal data for each of a plurality of anatomical landmarks associated with an activity engaged in by a person that defines a spatiotemporal trajectory of the anatomical landmark during the activity; modeling the acquired spatiotemporal data as a spatiotemporal graph (ST-Graph); and processing the ST-Graph using at least one non-local graph convolution neural network (NLGCN) to provide an identity for the person.

Abstract: Systems, methods, and computer readable media for identifying a person in a video are disclosed. Systems, methods, devices, and non-transitory computer readable media may include at least one processor that may be configured to generate a spatiotemporal emotion data compendium (STEM-DC) from the video and to process the STEM-DC using a deep fully adaptive graph convolutional network (FAGC) to determine a first person representation vector that represents the person in the video.

Abstract: A dynamic vision sensor (DVS) color camera (DVS-CCam) operable to acquire a color image of a scene, the DVS-CCam including a DVS photosensor comprising DVS pixels, an illuminator operable to transmit a light pattern characterized by temporal changes in intensity and color to illuminate a scene, an optical system configured to collect and focus on the pixels of the DVS photosensor light reflected by features in the scene from the light pattern transmitted by the illuminator, and a processor configured to process DVS signals generated by the DVS pixels responsive to temporal changes in the reflected light to provide a color image of the scene.

Abstract: Systems, methods, and computer readable media for identifying a person in a video are disclosed. Systems, methods, devices, and non-transitory computer readable media may include at least one processor that may be configured to generate a spatiotemporal emotion data compendium (STEM-DC) from the video and to process the STEM-DC using a deep fully adaptive graph convolutional network (FAGC) to determine a first person representation vector that represents the person in the video.

Abstract: Systems, methods, and computer readable media for identifying a person in a video are disclosed. Systems, methods, devices, and non-transitory computer readable media may include at least one processor that may be configured to generate a spatiotemporal emotion data compendium (STEM-DC) from the video and to process the STEM-DC using a deep fully adaptive graph convolutional network (FAGC) to determine a first person representation vector that represents the person in the video.

Abstract: A dynamic vision sensor (DVS) color camera (DVS-CCam) operable to acquire a color image of a scene, the DVS-CCam including a DVS photosensor comprising DVS pixels, an illuminator operable to transmit a light pattern characterized by temporal changes in intensity and color to illuminate a scene, an optical system configured to collect and focus on the pixels of the DVS photosensor light reflected by features in the scene from the light pattern transmitted by the illuminator, and a processor configured to process DVS signals generated by the DVS pixels responsive to temporal changes in the reflected light to provide a color image of the scene.

Abstract: A dynamic vision sensor (DVS) color camera (DVS-CCam) operable to acquire a color image of a scene, the DVS-CCam including a DVS photosensor comprising DVS pixels, an illuminator operable to transmit a light pattern characterized by temporal changes in intensity and color to illuminate a scene, an optical system configured to collect and focus on the pixels of the DVS photosensor light reflected by features in the scene from the light pattern transmitted by the illuminator, and a processor configured to process DVS signals generated by the DVS pixels responsive to temporal changes in the reflected light to provide a color image of the scene.

Abstract: A computer-implemented method of extracting high-frequency features from data, including receiving a first dataset; in a training phase, applying frequency-based guidance to learnable high-frequency filters in a neural network, the frequency based-guidance including promoting high eigenvalues associated with eigenvectors comprising the learnable high-frequency filters; extracting from the first dataset high-frequency features associated with the eigenvectors; and using the trained high-frequency filters to extract high-frequency features from a second dataset.

Abstract: System and method for imaging a target, the method comprising transmitting a plurality of N pulses of electromagnetic waves to illuminate the target, receiving a pulse of electromagnetic waves that is reflected by the target from each of the transmitted pulses at an imager sensitive to the electromagnetic waves, integrating energy in the plurality of received pulses during a same exposure period of the imager to provide a measure of the integrated energy, and processing the measure of integrated energy to provide N images of the target.

Abstract: A method of identifying a person, the method comprising: acquiring spatiotemporal data for each of a plurality of anatomical landmarks associated with an activity engaged in by a person that defines a spatiotemporal trajectory of the anatomical landmark during the activity; modeling the acquired spatiotemporal data as a spatiotemporal graph (ST-Graph); and processing the ST-Graph using at least one non-local graph convolution neural network (NLGCN) to provide an identity for the person.

Abstract: An imaging device is presented for use in an imaging system capable of improving the image quality. The imaging device has one or more optical systems defining an effective aperture of the imaging device. The imaging device comprises a lens system having an algebraic representation matrix of a diagonalized form defining a first Condition Number, and a phase encoder utility adapted to effect a second Condition Number of an algebraic representation matrix of the imaging device, smaller than said first Condition Number of the lens system.

Abstract: An imaging device is presented for use in an imaging system capable of improving the image quality. The imaging device has one or more optical systems defining an effective aperture of the imaging device. The imaging device comprises a lens system having an algebraic representation matrix of a diagonalized form defining a first Condition Number, and a phase encoder utility adapted to effect a second Condition Number of an algebraic representation matrix of the imaging device, smaller than said first Condition Number of the lens system.

Abstract: A device for authentication of a three-dimensional object includes an imaging array having a sensor configured to generate first and second sparse views of a surface of the three-dimensional object that faces the imaging array, and a processing circuitry. The processing circuitry is configured to: interpolate the first and second sparse views to obtain first and second interpolated images; calculate a planar disparity function for a plurality of image pixels of one of the first or second interpolated images; generate a projected image by displacing the plurality of image pixels of one of the first or the second interpolated images using the planar disparity function; and compare the projected image with the other of the first or second interpolated images to determine conformance of the planar disparity function with the interpolated images of the surface of the object.

Abstract: Presented herein are methods and systems for training a model, specifically a machine learning model, for example, a Deep Neural Network (DNN) for signal reconstruction in an iterative process comprising a plurality of training iterations and use of the trained DNN thereof. Each of the iterations comprises receiving a record associating a compressed signal created according to a sensing matrix selected from a plurality of sensing matrixes with a respective signal originated from a signal source and used for compressing the at least one compressed signal according to the selected sensing matrix, feeding the record and the sensing matrix to train a model and outputting the trained model which may be used for reconstructing one or more new signals originated from the signal source. Wherein at least two of the plurality of sensing matrixes are fed during at least two separate iterations of the plurality of training iterations.

Abstract: An imaging device is presented for use in an imaging system capable of improving the image quality. The imaging device has one or more optical systems defining an effective aperture of the imaging device. The imaging device comprises a lens system having an algebraic representation matrix of a diagonalized form defining a first Condition Number, and a phase encoder utility adapted to effect a second Condition Number of an algebraic representation matrix of the imaging device, smaller than said first Condition Number of the lens system.

Abstract: An imaging device is presented for use in an imaging system capable of improving the image quality. The imaging device has one or more optical systems defining an effective aperture of the imaging device. The imaging device comprises a lens system having an algebraic representation matrix of a diagonalized form defining a first Condition Number, and a phase encoder utility adapted to effect a second Condition Number of an algebraic representation matrix of the imaging device, smaller than said first Condition Number of the lens system.

Abstract: A light field imaging system and method are presented. The light-field imaging system comprises an optical arrangement configured for collecting an input light field from a scene and projecting collected light on a pixel matrix of a detector unit, the optical arrangement comprising” an optical coder configured for applying angular coding to the light being collected to produce angularly coded light by separating the light being collected into an array of u angular light components corresponding to u different discrete viewpoints of the scene and projecting light from all of said u angular light components onto each of at least some of the pixels in the pixel matrix thereby causing in-pixel summation of the u angular light components on the pixel matrix of the sensor unit.

Abstract: Disclosed herein is a novel a tunable Micro-Electro-Mechanical (MEMS) Etalon system including: a functional layer patterned to define a suspension structure for suspending a first mirror being an aperture mirror of the Etalon, an aperture mirror coupled to the suspension structure, and a back layer including a second mirror, being a back mirror of the Etalon. The functional layer may be located above the back layer and the back layer may include spacer structures protruding therefrom towards the aperture mirror to define a minimal gap between the aperture mirror and the back mirror and prevent collision between them. The aspect ratio between the width of the etalon/mirrors may be high (e.g. at least 500), and the minimal gap/distance between the mirrors may be small in the order of tens of nanometers (nm).