In accordance with an aspect of the present invention, an information processing apparatus includes an interface that transmits or receives data to or from an input apparatus that inputs user position information indicating a position of a user, and a processor that calculates an evaluation value for an evaluation target position based on the user position information, determines a position in a virtual space or a real space where an agent supporting the user is placed based on the evaluation value, and places the agent at the determined position in the virtual space or real space.

Methods and systems disclosed herein relate generally to processing images to estimate whether at least part of a tumor is represented in the images. A computer-implemented method includes accessing an image of at least part of a biological structure of a particular subject, processing the image using a segmentation algorithm to extract a plurality of image objects depicted in the image, determining one or more structural characteristics associated with an image object of the plurality of image objects, processing the one or more structural characteristics using a trained machine-learning model to generate estimation data corresponding to an estimation of whether the image object corresponds to a lesion or tumor associated with the biological structure, and outputting the estimation data for the particular subject.

The present disclosure provides a method and apparatus for detecting a traffic anomaly, a device, a storage medium and a computer program product, relates to the field of artificial intelligence, and specifically to computer vision and deep learning technologies, and can be applied to intelligent transportation scenarios. A specific implementation of the method comprises: acquiring a traffic video stream; performing vehicle detection tracking on the traffic video stream to determine whether there is an abnormally stopped vehicle, wherein a stop with a time length exceeding a preset time length belongs to an abnormal stop; and performing a traffic anomaly classification on a video frame corresponding to the abnormal stop using a decision tree to obtain a traffic anomaly type, if there is the abnormally stopped vehicle, wherein the decision tree is generated based on features for a traffic anomaly detection.

An electronic device may include a communication module, a camera, and at least one processor operably coupled with the communication module and the camera. The at least one processor may be configured to establish a communication connection with an external device and a wearable device by using the communication module, and acquire an image through the camera, and acquire first object information for rendering a first virtual reality object based on the image, and transmit the first object information to the external device through the communication module, and while transmitting the first object information to the external device, determine whether a specified event occurs, and provide map information on a peripheral environment of the electronic device based on at least one of first peripheral space information including information on an object located within a first region, acquired by using the camera, or second peripheral space information including information on an object located within a second region, received from the wearable device, and based on the occurrence of the specified event, transmit second object information which includes the map information and information on a location of the first virtual reality object within the map information, to the external device.

Described are examples for detecting objects in an image on a device including setting, based on a condition, a number of sparse proposals to use in performing object detection in the image, performing object detection in the image based on providing the sparse proposals as input to an object detection process to infer object location and classification of one or more objects in the image, and indicating, to an application and based on an output of the object detection process, the object location and classification of the one or more objects.

The optical control apparatus includes a light source, a light collecting section, and an optical path control section. The light source emits light. The light collecting section collects the light emitted from the light source and illuminates the light onto an object.

A display system (1) includes a display (D1) installed on a first road (R1) and a camera (C1) for capturing an image of a second road (R2) being narrower than the first road (R1), in which the display (D1) acquires and displays an image generated by the camera (C1) or a display data using the image.

A video shooting method, a video shooting apparatus, and an electronic device are provided. The video shooting method includes: receiving a first input performed on a target video preview image, and shooting, in response to the first input, a target video according to N objects selected by the first input in the target video preview image, where the target video includes N sub-videos corresponding to the N objects, each of the sub-videos includes one of the N objects, a shooting range of each of the sub-videos is a range corresponding to a part of screen in the target video preview image, and N is an integer greater than 1.

An object position estimation device (1) is provided with: a feature extraction unit (10) including a first feature extraction unit (21) which generates a first feature map by subjecting a target image to a convolution computation process, and a second feature extraction unit (22) which generates a second feature map by also subjecting the first feature map to the convolution computation process; and a likelihood map estimation unit (20) including a first position likelihood estimation unit (23) which, by using the first feature map, estimates a first likelihood map indicating the probability that first objects having a first size are present in the target image, and a second position likelihood estimation unit (24) which, by using the second feature map, estimates a second likelihood map indicating the probability that second objects having a second size, which is greater than the first size, are present in the target image.

Aspects of the present disclosure relate generally to a vision system that detects persons exiting an environment, comprising detecting, by a processor using a plurality of image frames from at least one sensor, persons that exited the environment during a first period of time. The vision system further determines, by the processor, an exit count for the first period of time and retrieves, from a database, historical egress data comprising a detected historic exit count and a corrected historic exit count of the environment for a second period of time corresponding to the first period of time. The vision system calculates an error rate for the second period of time based on a ratio of the detected historic exit count and the corrected historic exit count, determines and stores a corrected exit count for the first period of time by adjusting the exit count using the error rate.