Abstract: A system for producing a virtual image view for a vehicle is provided. The system includes one or more image capture means configured to capture image data in proximity to the vehicle, the image data being defined at least in part by first viewpoint parameters, and to provide an identifier identifying the respective one or more image capture means, storage means configured to store a plurality of virtualization records containing conversion information related to a virtualized viewpoint and a plurality of image capture means, and processing means.

Abstract: A system for generating a mask for object instances in an image is provided. The system includes a first module comprising a trained neural network and configured to input the image to the neural network, wherein the neural network is configured to generate: pixel offset vectors for the pixels of the object instance configured to point towards a unique center of an object instance, the pixel offset vectors thereby forming a cluster with a cluster distribution, and for each object instance an estimate of said cluster distribution defining a margin for determining which pixels belong to the object instance. A method for training a neural network map to be used for generating a mask for object instances in an image is also provided.

Abstract: A computer-implemented method for training a classifier (??), including: training a pretext model (??) to learn a pretext task, so as to minimize a distance between an output of a source sample via the pretext model (??) and an output of a corresponding transformed sample via the pretext model (??), the transformed sample being a sample obtained by applying a transformation (T) to the source sample; S20) determining a neighborhood (NXi) of samples (Xi) of a dataset (SD) in the embedding space; S30) training the classifier (??) to predict respective estimated probabilities ??j(Xi), j=1 . . . C, for a sample (Xi) to belong to respective clusters (Cj), by using a second training criterion which tends to: maximize a likelihood for a sample and its neighbors (Xj) of its neighborhood (Nxi) to belong to the same cluster; and force the samples to be distributed over several clusters.

Abstract: A method for adjusting an information system of a mobile machine, the information system being configured to calculate 3D information relative to a scene in which the mobile machine is moving, the method including: acquiring at least a first image of the scene at a first time and a second image of the scene at a second time; detecting one or more scene features in the first image and the second image; matching the one or more scene features across the first image and the second image based upon detection of the one or more scene features; estimating an egomotion of the mobile machine based upon the matching of the one or more scene features across the first image and the second image; and adjusting the information system by taking into account the estimation of the egomotion of the mobile machine.

Abstract: A method for tracking a position of an object in a scene surrounding a mobile machine based upon information acquired from monocular images, includes: acquiring at least a first image at a first time and a second image at a second time, the first image and the second image each including image data corresponding to the object and a scene feature present in the scene surrounding the mobile machine; detecting the object in the first image and the second image; matching the scene feature across the first image and the second image; performing an estimation of an egomotion of the mobile machine based upon the scene feature matched across the first image and the second image; and predicting a position of the object taking into account the estimation of the egomotion of the mobile machine.

Abstract: A system and a method for processing a plurality of images, each image of the plurality of images being acquired by a respective image acquisition module of a vehicle and each image acquisition module being oriented outwardly with respect to the vehicle, the method comprising: elaborating a bird's eye view image of surroundings of the vehicle using pixel values of pixels of at least one portion of each image of the plurality of images as pixel values of the bird's eye view image, and performing, on the bird's eye view image, a detection of at least one lane marked on a surface on which the vehicle is and visible on the bird's eye view image.

Abstract: A system and a method for processing an image so as to perform instance segmentation. The system/method includes: a—inputting (S1) the image (IMG) to a first neural network configured to output an affinity graph (AF), and b—inputting (S2), to a second neural network, the affinity graph and a predefined seed-map (SM), so as to determine whether other pixels belong to a same instance, and set at a first value the value of the other pixels determined as belonging to the same instance.

Abstract: A computer-implemented method for calculating information relative to a relative speed between an objectand a camera, based on two images of the object acquired by the camera. The method comprises:determining a value of an optical flowbetween the two images and, altogether with or after the determination of the value the optical flow, determining at least one parameter of the transformation, using the optical flow; andcalculating information relative to a relative speed between the object and the camera, based on said at least one parameter of the transformation.

Abstract: A system and a method for processing a plurality of images, each image of the plurality of images being acquired by a respective image acquisition module of a vehicle and each image acquisition module being oriented outwardly with respect to the vehicle, the method comprising: elaborating a bird's eye view image of surroundings of the vehicle using pixel values of pixels of at least one portion of each image of the plurality of images as pixel values of the bird's eye view image, and performing, on the bird's eye view image, a detection of at least one lane marked on a surface on which the vehicle is and visible on the bird's eye view image.

Abstract: A system for producing a virtual image view for a vehicle is provided. The system includes one or more image capture means configured to capture image data in proximity to the vehicle, the image data being defined at least in part by first viewpoint parameters, and to provide an identifier identifying the respective one or more image capture means, storage means configured to store a plurality of virtualization records containing conversion information related to a virtualized viewpoint and a plurality of image capture means, and processing means.

Abstract: A system for generating a mask for object instances in an image is provided. The system includes a first module comprising a trained neural network and configured to input the image to the neural network, wherein the neural network is configured to generate: pixel offset vectors for the pixels of the object instance configured to point towards a unique center of an object instance, the pixel offset vectors thereby forming a cluster with a cluster distribution, and for each object instance an estimate of said cluster distribution defining a margin for determining which pixels belong to the object instance. A method for training a neural network map to be used for generating a mask for object instances in an image is also provided.

Abstract: A computer-implemented method for training a classifier (??), including: training a pretext model (??) to learn a pretext task, so as to minimize a distance between an output of a source sample via the pretext model (??) and an output of a corresponding transformed sample via the pretext model (??), the transformed sample being a sample obtained by applying a transformation (T) to the source sample; S20) determining a neighborhood (NXi) of samples (Xi) of a dataset (SD) in the embedding space; S30) training the classifier (??) to predict respective estimated probabilities ??j(Xi), j=1 . . . C, for a sample (Xi) to belong to respective clusters (Cj), by using a second training criterion which tends to: maximize a likelihood for a sample and its neighbors (Xj) of its neighborhood (Nxi) to belong to the same cluster; and force the samples to be distributed over several clusters.

Abstract: A system and a method for processing an image so as to perform instance segmentation. The system/method includes: a—inputting (S1) the image (IMG) to a first neural network configured to output an affinity graph (AF), and b—inputting (S2), to a second neural network, the affinity graph and a predefined seed-map (SM), so as to determine whether other pixels belong to a same instance, and set at a first value the value of the other pixels determined as belonging to the same instance.

Abstract: The present disclosure provides a method for processing at least one image comprising inputting the image to at least one neural network, the at least one network being configured to deliver, for each pixel of a group of pixels belonging to an object of a given type visible on the image, an estimation of object parameters that are parameters of the object. The method further comprising processing the estimations of the object parameters using an instance segmentation mask identifying instances of objects having the given type.