An information processing device includes circuitry to obtain coordinates of an attention point in an area of which a video image is to be displayed, based on information indicating a designated area and received from a terminal device, select an image capturing device based on the coordinates of the attention point, and obtain an angle of view in which angles each of which is formed by a corresponding one of two first straight lines and a second straight line are adjusted to have same degrees with each other. Each first straight line connects the image capturing device and a corresponding one of ends of the area. The second straight line connects the image capturing device and the attention point. The circuitry determines a display area adjusted based on the angle of view and transmits, to the terminal device, information on the image capturing device and information on the display area.

A deep neural network(s) (DNN) may be used to detect objects from sensor data of a three dimensional (3D) environment. For example, a multi-view perception DNN may include multiple constituent DNNs or stages chained together that sequentially process different views of the 3D environment. An example DNN may include a first stage that performs class segmentation in a first view (e.g., perspective view) and a second stage that performs class segmentation and/or regresses instance geometry in a second view (e.g., top-down). The DNN outputs may be processed to generate 2D and/or 3D bounding boxes and class labels for detected objects in the 3D environment. As such, the techniques described herein may be used to detect and classify animate objects and/or parts of an environment, and these detections and classifications may be provided to an autonomous vehicle drive stack to enable safe planning and control of the autonomous vehicle.

A traffic signal recognition method and a traffic signal recognition device acquire a result sequence consisting of a plurality of determination results in time-series order obtained by determining a display state of a traffic signal based on a plurality of images of a traveling direction of a vehicle, set a first threshold as a number threshold if a first distance from a position of the vehicle to a position of a stop line corresponding to the traffic signal is equal to or greater than a distance threshold, set a second threshold smaller than the first threshold as the number threshold if the first distance is less than the distance threshold, and output a latest determination result if a number of determination results identical to the latest determination result is greater than the number threshold, among the determination results.

A method for automatically creating an equipment inventory from a plurality of images of a site. The method includes receiving, by a processor, the plurality of images of the site, labeling, by the processor, equipment present in each image of the plurality of images of the site, creating, by the processor, an overlap curve for neighboring images of the plurality of images of the site, determining, by the processor, a useful image of each side of the site based on the overlap curve, the useful image including a subset of the plurality of images, creating, by the processor, an overlay of equipment in the useful image of each side of the site, counting, by the processor, equipment present in the overlay of each side of the site, and generating, by the processor, the equipment inventory by adding a number of equipment counted in the overlay of each side of the site.

The present invention provides a user with a user interface for enabling the user to efficiently perform an operation for generating a virtual viewpoint image for each imaging target subject to be imaged by a plurality of imaging apparatuses. An event information acquisition unit acquires information about an event subjected to virtual viewpoint image generation, and transmits the acquired event information to a user interface (UI) determination unit. The UI determination unit determines a UI to be generated by a UI generation unit based on the event information transmitted from the event information acquisition unit. The UI generation unit generates the UI determined by the UI determination unit. The user performs an input operation for generating a virtual viewpoint image according to the UI generated by the UI generation unit.

The invention relates to a method that allows a set of embryos to be ranked on the basis of ploidy potential and/or pregnancy generation potential, to aid the process of selecting embryos for transfer in an in-vitro fertilisation procedure. The method measures properties or characteristics of the entire blastocyst; extracts characteristics by identifying different cell types, mainly blastocyst structures and patterns, without extracting characteristics of the first cell divisions and the behaviour thereof over time; and predicts the prognosis of pregnancy and/or ploidy (result of genetic study and successful implantation), using micrographs standardised for the management thereof and by means of sequential preprocessing and machine learning algorithms implemented in a computer in order to rank the potential of a set of embryos, to obtain a successful, live, full-term pregnancy.

An image processing apparatus includes: a first obtainment unit configured to obtain a plurality of captured images of an object captured under a plurality of image capture conditions; a second obtainment unit configured to obtain information on a material of the object; and an estimation unit configured to estimate a distribution of normals of the object based on the plurality of captured images and the information on the material.

The present application relates to an image-fusion method and apparatus, a computing and processing device and a storage medium. The method includes: based on a same one target scene, acquiring a plurality of exposed images of different exposure degrees; acquiring a first exposed-image fusion-weight diagram corresponding to each of the exposed images, wherein the first exposed-image fusion-weight diagram contains fusion weights corresponding to pixel points of the exposed image; acquiring a region area of each of overexposed regions in each of the exposed images; for each of the exposed images, by using the region area of each of the overexposed regions in the exposed image, performing smoothing filtering to the first exposed-image fusion-weight diagram corresponding to the exposed image, to obtain a second exposed-image fusion-weight diagram corresponding to the exposed image; and according to each of the second exposed-image fusion-weight diagrams, performing image-fusion processing to the plurality of exposed images, to obtain a fused image. Accordingly, the present application can balance the characteristics of the different overexposed regions, and prevent the losing of the details of the small overexposed regions, to enable the obtained fused image to be more realistic.

A computer implemented method includes obtaining data for raw image frames captured by a moving camera. The raw image frames are indexed geographically, and a graph is created from the multiple raw image frames. The graph includes image frames as vertices and edges that represent image frames having overlapping image information. The method further includes skipping frames based on the amount of overlap, determining a frame having an interesting feature, using the graph to find additional raw image frames that have the interesting feature, combining multiple raw image frames to form a unique image frame, and transmitting the unique image frame.

A computer, including a processor and a memory, the memory including instructions to be executed by the processor to acquire one or more images from a camera and determine first camera noise values based on the one or more images by determining reactions of camera photo receptors to light. The instructions can include further instructions to compare the first camera noise values with second camera noise values determined based on previously acquired images from the camera and output a tamper determination for the camera based on whether the first camera noise values match, within a tolerance value, the second camera noise values determined based on the previously acquired images from the camera.