A processor-implemented method of tracking a target object includes: extracting a feature from frames of an input image; selecting one a neural network model from among a plurality of neural network models that is provided in advance based on a feature value range, based on a feature value of a target object that is included in the feature of a previous frame among the frames; and generating a bounding box of the target object included in a current frame among the frames, based on the selected neural network model.

Method and system for identifying an empty region in a label and placing a content thereon is provided. The method includes processing an image of the label to extract label attribute and the content to retrieve content attribute. Label attribute includes at least one of dimensions of the label, at least one pre-existing content on the label, dimensions associated with pre-existing content, and location of pre-existing content on the label. The content attribute includes a type of content, dimensions of content, a preferred label location associated with content. The method further includes determining at least one empty region within the label, based on extracted label attribute and the retrieved content attribute. Each of the at least one empty region may be configured to accommodate the content. The method further includes inserting the content into one of the at least one empty region based on a predefined rule.

Provided are a training method and apparatus for an image processing image processing model, and an image processing method and apparatus. The training method comprises: acquiring a sample image and a first reference image, wherein the information quantity and resolution of the sample image are respectively lower than those of the first reference image; inputting the sample image into a generative network in an image processing model, and carrying out super-resolution processing and down-sampling processing on the sample image by means of the generative network, so as to generate and output at least one result image; determining the total image loss of the at least one result image according to the first reference image; and adjusting parameters of the generative network according to the total image loss, so that the total image loss of at least one result image output by the adjusted generative network meets an image loss condition.

This image processing apparatus is provided with an image acquisition unit for generating a concentration change image and a control unit for performing control for displaying a blood vessel image and a concentration change image, and the control unit is configured to perform control for accepting a selection of a target region on the blood vessel image displayed on the display unit and for displaying the concentration change image corresponding to the selected target region.

A system for reducing a search area for identifying correspondences identifies an overlap region within a first match frame captured by a match camera. The overlap region includes one or more points of the first match frame that are associated with one or more same portions of an environment as one or more corresponding points of a first reference frame captured by a reference camera. The system obtains a second reference frame captured by the reference camera and a second match frame captured by the match camera. The system identifies a reference camera transformation matrix, and/or a match camera transformation matrix. The system defines a search area within the second match frame based on the overlap region and the reference camera transformation matrix and/or the match camera transformation matrix.

Methods for vehicle data collection by image analysis are provided. An example method involves positioning a camera in a vehicle to be pointed toward a field of interest in the vehicle, capturing an image of the field of interest with the camera, identifying a region of interest in the image that is expected to convey vehicle information, and running an image processing model over the region of interest to extract vehicle information from the image.

An information processing apparatus includes a processor configured to: acquire positional information indicating a position of a portion of an article; acquire condition information indicating a condition of the portion of the article; and store the positional information and the condition information in association with each other.

A computing system for detecting objects in an image can perform operations including generating an image pyramid that includes a first level corresponding with the image at a first resolution and a second level corresponding with the image at a second resolution. The operations can include tiling the first level and the second level by dividing the first level into a first plurality of tiles and the second level into a second plurality of tiles; inputting the first plurality of tiles and the second plurality of tiles into a machine-learned object detection model; receiving, as an output of the machine-learned object detection model, object detection data that includes bounding boxes respectively defined with respect to individual ones of the first plurality of tiles and the second plurality of tiles; and generating image object detection output by mapping the object detection data onto an image space of the image.

Provided are an image detection device, an image detection method and a program, which are capable of improving correspondence to a target deformation by optimizing a template shape, when performing target detection using template matching. An image detection device 100 for detecting a target from an input image comprises: a template generation unit 10 that generates a template for detecting a target; a mask generation unit 20 that generates a mask which shields a portion of the template, on the basis of temporal variations of a feature point extracted from an area including the image target; and a detection unit 30 that detects the target from the image using the template a portion of which is shielded by the mask.

An anchor determination includes: performing feature extraction on an image to be processed to obtain a first feature map of the image to be processed; performing anchor prediction on the first feature map via an anchor prediction network to obtain position information of anchors and shape information of the anchors in the first feature map, the position information of the anchors referring to information about positions in the first feature map where the anchors are generated. A corresponding anchor determination apparatus and a storage medium are also provided.