A device and computer-implemented method for object tracking. The method comprises providing a sequence of digital images, determining a sequence of relational graph embeddings, wherein a first relational graph embedding of the sequence comprises a first object embedding representing a first object in a first digital image of the sequence of digital images, wherein the first relational graph embedding comprises a first relation embedding of a relation for the first object embedding, wherein the first relation embedding relates the first object embedding to embeddings representing other objects of the first digital image in the first relational graph embedding and to embeddings in a second relational graph embedding of the sequence that represent objects of a second digital image of the sequence of digital images.

The invention relates to a computing system and a computer-implemented method for classifying images of retina of eyes of subjects. A captured image of a retina is processed to obtain a plurality of different segmented images each having different selected portions of the captured image using different selection rules. The multiple segmented images are provided to respective dedicated machine learning models to output an image classification based on the respective segmented images provided as input. An ensemble classification is determined based on the multiple classifications obtained by means of the multiple trained machine learning models.

A high-definition map creation method includes: obtaining point cloud data collected with respect to a target region, the point cloud data including K frames of point clouds and an initial pose of each frame of point cloud, K being an integer greater than 1; associating the K frames of point clouds with each other in accordance with the initial pose to obtain a first point cloud relation graph of the K frames of point clouds; performing point cloud registration on the K frames of point clouds in accordance with the first point cloud relation graph and the initial pose to obtain a target relative pose of each frame of point cloud in the K frames of point clouds; and splicing the K frames of point clouds in accordance with the target relative pose to obtain a point cloud map of the target region.

Systems and methods for operating an autonomous vehicle. The methods comprising: obtaining, by a computing device, loose-fit cuboids overlaid on 3D graphs so as to each encompass LiDAR data points associated with a given object; defining, by the computing device, an amodal cuboid based on the loose-fit cuboids; using, by the computing device, the amodal cuboid to train a machine learning algorithm to detect objects of a given class using sensor data generated by sensors of the autonomous vehicle or another vehicle; and causing, by the computing device, operations of the autonomous vehicle to be controlled using the machine learning algorithm.

Automatic substance preparation and evaluation systems and methods are provided for preparing and evaluating a fluidic substance, such as e.g. a sample with bodily fluid, in a container and/or in a dispense tip. The systems and methods can detect volumes, evaluate integrities, and check particle concentrations in the container and/or the dispense tip.

Embodiments include a method for self-rectification of a stereo camera, wherein the stereo camera comprises a first camera and a second camera, the method comprises creating image pairs from a first images taken by the first camera and second images taken by the second camera, respectively, such that each image pair comprises two images taken at essentially the same time by the first camera and the second camera, respectively. The method comprises creating, for each image pair, matching point pairs from corresponding points in the two images of each image pair, such that each matching point pair comprises one point from each of the first and second image of the respective image pair. For each matching point pair, a disparity is calculated and a plurality of disparities is created for each image pair, and the resulting plurality of disparities is taken into account for the self-rectification.

Embodiments relate to an architecture of a vision pipe included in an image signal processor. The architecture includes a front-end portion that includes a pair of image signal pipelines that generate an updated luminance image data. A back-end portion of the vision pipe architecture receives the updated luminance images from the front-end portion and performs, in parallel, scaling and various computer vision operations on the updated luminance image data. The back-end portion may repeatedly perform this parallel operation of computer vision operations on successively scaled luminance images to generate a pyramid image.

Systems, devices, and methods are presented for segmenting an image of a video stream with a client device by receiving one or more images depicting an object of interest and determining pixels within the one or more images corresponding to the object of interest. The systems, devices, and methods identify a position of a portion of the object of interest and determine a direction for the portion of the object of interest. Based on the direction of the portion of the object of interest, a histogram threshold is dynamically modified for identifying pixels as corresponding to the portion of the object of interest. The portion of the object of interest is replaced with a graphical interface element aligned with the direction of the portion of the object of interest.

An image segmentation method is disclosed that allows a user to select image component types, for example tissue types and or background, and have the method of the present invention segment the image according to the user's input utilizing the superpixel image feature data and spatial relationships.

The application discloses a method and system for segmenting a lung image. The method may include obtaining a target image relating to a lung region. The target image may include a plurality of image slices. The method may also include segmenting the lung region from the target image, identifying an airway structure relating to the lung region, and identifying one or more fissures in the lung region. The method may further include determining one or more pulmonary lobes in the lung region.