Abstract: In various examples, sensor data representative of an image of a field of view of a vehicle sensor may be received and the sensor data may be applied to a machine learning model. The machine learning model may compute a segmentation mask representative of portions of the image corresponding to lane markings of the driving surface of the vehicle. Analysis of the segmentation mask may be performed to determine lane marking types, and lane boundaries may be generated by performing curve fitting on the lane markings corresponding to each of the lane marking types. The data representative of the lane boundaries may then be sent to a component of the vehicle for use in navigating the vehicle through the driving surface.

Abstract: In various examples, sensor data representative of an image of a field of view of a vehicle sensor may be received and the sensor data may be applied to a machine learning model. The machine learning model may compute a segmentation mask representative of portions of the image corresponding to lane markings of the driving surface of the vehicle. Analysis of the segmentation mask may be performed to determine lane marking types, and lane boundaries may be generated by performing curve fitting on the lane markings corresponding to each of the lane marking types. The data representative of the lane boundaries may then be sent to a component of the vehicle for use in navigating the vehicle through the driving surface.

Abstract: In various examples, sensor data representative of an image of a field of view of a vehicle sensor may be received and the sensor data may be applied to a machine learning model. The machine learning model may compute a segmentation mask representative of portions of the image corresponding to lane markings of the driving surface of the vehicle. Analysis of the segmentation mask may be performed to determine lane marking types, and lane boundaries may be generated by performing curve fitting on the lane markings corresponding to each of the lane marking types. The data representative of the lane boundaries may then be sent to a component of the vehicle for use in navigating the vehicle through the driving surface.

Abstract: In various examples, sensor data representative of an image of a field of view of a vehicle sensor may be received and the sensor data may be applied to a machine learning model. The machine learning model may compute a segmentation mask representative of portions of the image corresponding to lane markings of the driving surface of the vehicle. Analysis of the segmentation mask may be performed to determine lane marking types, and lane boundaries may be generated by performing curve fitting on the lane markings corresponding to each of the lane marking types. The data representative of the lane boundaries may then be sent to a component of the vehicle for use in navigating the vehicle through the driving surface.

Abstract: The technology disclosed relates to tracking motion of a wearable sensor system using a combination a RGB (red, green, and blue) and IR (infrared) pixels of one or more cameras. In particular, it relates to capturing gross features and feature values of a real world space using RGB pixels and capturing fine features and feature values of the real world space using IR pixels. It also relates to enabling multi-user collaboration and interaction in an immersive virtual environment. It also relates to capturing different sceneries of a shared real world space from the perspective of multiple users. It further relates to sharing content between wearable sensor systems. In further relates to capturing images and video streams from the perspective of a first user of a wearable sensor system and sending an augmented version of the captured images and video stream to a second user of the wearable sensor system.

Abstract: In various examples, sensor data representative of an image of a field of view of a vehicle sensor may be received and the sensor data may be applied to a machine learning model. The machine learning model may compute a segmentation mask representative of portions of the image corresponding to lane markings of the driving surface of the vehicle. Analysis of the segmentation mask may be performed to determine lane marking types, and lane boundaries may be generated by performing curve fitting on the lane markings corresponding to each of the lane marking types. The data representative of the lane boundaries may then be sent to a component of the vehicle for use in navigating the vehicle through the driving surface.

Abstract: The technology disclosed relates to tracking motion of a wearable sensor system using a combination a RGB (red, green, and blue) and IR (infrared) pixels of one or more cameras. In particular, it relates to capturing gross features and feature values of a real world space using RGB pixels and capturing fine features and feature values of the real world space using IR pixels. It also relates to enabling multi-user collaboration and interaction in an immersive virtual environment. It also relates to capturing different sceneries of a shared real world space from the perspective of multiple users. It further relates to sharing content between wearable sensor systems. In further relates to capturing images and video streams from the perspective of a first user of a wearable sensor system and sending an augmented version of the captured images and video stream to a second user of the wearable sensor system.

Abstract: A technology for tracking motion of a wearable sensor system uses a combination a RGB (red, green, and blue) and IR (infrared) pixels of one or more cameras. In particular, it relates to capturing gross features and feature values of a real world space using RGB pixels and capturing fine features and feature values of the real world space using IR pixels. It also relates to enabling multi-user collaboration and interaction in an immersive virtual environment. It also relates to capturing different sceneries of a shared real world space from the perspective of multiple users. It further relates to sharing content between wearable sensor systems. In further relates to capturing images and video streams from the perspective of a first user of a wearable sensor system and sending an augmented version of the captured images and video stream to a second user of the wearable sensor system.

Abstract: A method for recognition between a first and second object represented by at least one first image and at least one second image, includes defining rectangular assemblies of random pixels; filtering the first image with first n filters obtained from the assemblies of pixels to obtain n first filtered matrices; classifying the n first filtered matrices by providing a first center and a first radius within a space of N dimensions; filtering the second image with the first n filters to obtain n second filtered matrices; classifying the n second filtered matrices by providing a second center within the space of N dimensions; and comparing the first center and first radius with the second center.

Abstract: A recognition method for recognition between a first and second object represented by at least one first image and at least one second image, includes: defining a plurality of rectangular assemblies of random pixels; filtering the first image with first n filters obtained from the assemblies of pixels to obtain n first filtered matrices; classifying the n first filtered matrices by providing a first centre and a first radius within a space of N dimensions; filtering the second image with the first n filters to obtain n second filtered matrices; classifying the n second filtered matrices by providing a second centre within the space of N dimensions; comparing the first centre and first radius with the second centre; whereby recognition between the object and second object has if at least one second centre lies at a distance from the first centre which is less than or equal to at least one first radius.