Disclosed is a computing device for generating region information of at least one object included in an image, the computing device including: a memory including computer-executable components; and a processor for executing the computer-executable components stored in the memory, in which the computer-executable components include: a key point heat map generation component for generating, for the image, a key point heat map including key point information of the at least one object; a non-rotating bounding box generation component for generating a non-rotating bounding box based on the generated key point heat map; a rotating bounding box generation component for generating a rotating bounding box by using the non-rotating bounding box; and a final bounding box generation component for representing a region occupied by the at least one object in the image by using at least one of the non-rotating bounding box or the rotating bounding box.

Provided are an illegal building identification method and apparatus, a device, and a storage medium, which relate to the field of cloud computing. The specific implementation scheme is: acquiring a target image and a reference image associated with the target image; extracting a target building feature of the target image and a reference building feature of the reference image, respectively; and determining, according to the target building feature and the reference building feature, an illegal building identification result of the target image.

A vehicle speed and/or slip of a vehicle is determined. At least first and second images of a traveling surface captured by an image sensor using an optical lens are received by a processor. The images are used to determine the distance travelled by one or more pixels in first and second directions. These distances, along with the sampling rate of the image sensor, determine pixel speed in the first and second directions. The pixel speeds in the first direction, the second direction, or both, may be used to calculate the vehicle speed, a side slip angle, or a longitudinal slip. A tire speed may also be similarly determined using a second image sensor, a second optical lens, and a second processor capturing images of the tire surface. These vehicle parameters may be used for testing or operating the vehicle, including modifying vehicle behavior during operation.

A system, a method and a non-transitory computer readable storage medium for base calling are described. The base calling method includes processing through a neural network first image data comprising images of clusters and their surrounding background captured by a sequencing system for one or more sequencing cycles of a sequencing run. The base calling method further includes producing a base call for one or more of the clusters of the one or more sequencing cycles of the sequencing run.

The disclosed feature makes it possible to accurately determine a change that has occurred in a tissue. The feature includes: a binarizing section (41) that generates, from an image to be analyzed, a plurality of binarized images having respective binarization reference values different from each other; a Betti number calculating section (42) that calculates, for each of the plurality of binarized images, a one-dimensional Betti number indicating the number of hole-shaped regions each of which is surrounded by pixels each having a first pixel value obtained by binarization and is constituted by pixels each having a second pixel value obtained by binarization; and a determining section (44) that determines a change that has occurred in the tissue, based on a binarization reference value and a one-dimensional Betti number in a binarized image in which the one-dimensional Betti number is maximized.

A computer implemented method and apparatus for determining a location at which a given feature is represented in medical imaging data is disclosed. A first descriptor for a first location in first medical imaging data is obtained. The first location is the location within the first medical imaging data at which the given feature is represented. A second descriptor for each of a plurality of candidate second locations in second medical imaging data is obtained. A similarity metric indicating a degree of similarity with the first descriptor is calculated for each of the plurality of candidate second locations. A candidate second location is selected from among the plurality of candidate second locations based on the calculated similarity metrics. The location at which the given feature is represented in the second medical imaging data is determined based on the selected candidate second location.

The invention provides a sample observation system including a scanning electron microscope and a calculator. The calculator: (1) acquires a plurality of images captured by the scanning electron microscope; (2) acquires, from the plurality of images, a learning defect image including a defect portion and a learning reference image not including the defect portion; (3) calculates estimation processing parameters by using the learning defect image and the learning reference image; (4) acquires an inspection defect image including a defect portion; and (5) estimates a pseudo reference image by using the estimation processing parameters and the inspection defect image.

Crop loss estimation allows a user to monitor and estimate damage to the crops due to various natural events/factors. State of the art systems used for the crop loss estimation have the disadvantage that they do not convey to the users extent of damage. In addition to this, the existing methods do not take into account the recovery factor of the crops due to multiple factors and end up in overestimating the loss. The disclosure herein generally relates to crop monitoring, and, more particularly, to a method and system for crop loss estimation. In this method, crop loss is assessed based on real-time weather parameters and remote sensing data collected and processed, and crops are classified as being in one of a repairable damage class and a permanent damage class. The system also quantifies the crop loss, which allows the user to understand magnitude of the crop loss.

According to an aspect of the present disclosure, there is provided a method of generating training data related to an artificial intelligence-based detection model, which includes the steps of: generating three-dimensional (3D) model of a hidden target object and 3D model of a hiding tool object, respectively, and combining the 3D model of the hidden target object and the 3D model of the hiding tool object; generating a two-dimensional (2D) image by capturing, in at least one direction, the combined 3D model obtained by combining the 3D model of the hidden target object with the 3D model of the hiding tool object; and processing the generated 2D image with reference to deformation or distortion which occurs in a detection target image obtained by actually capturing a detection target object to be detected.

An analysis device includes a processor configured to: execute a first learning process on a generative model for images such that the images that bring a recognition result of an image recognition process into a preassigned state are generated; execute a second learning process on the generative model on which the first learning process has been executed, while gradually changing recognition accuracy of the images generated by the generative model on which the first learning process has been executed, to desired recognition accuracy; acquire each piece of information on back-error propagation calculated by executing the image recognition process, for the images with each level of the recognition accuracy generated through a course of the second learning process; and generate evaluation information indicating each of image parts that cause erroneous recognition at each level of the recognition accuracy, based on the acquired each piece of the information on the back-error propagation.