An image processing apparatus according to a first aspect of the present invention acquires a first image and a second image in a first image acquisition mode, or a second image acquisition mode in which a second image acquisition ratio is higher than in the first image acquisition mode, on the basis of a detection result of a specific target (whether or not a specific target has been detected, what type of specific target). For example, in accordance with whether or not a specific target has been detected and the type of specific target, the first image and the second image can be acquired in the first image acquisition mode in a case where the necessity for acquiring the second image is low, whereas the first image and the second image can be acquired in the second image acquisition mode, in which the second image acquisition ratio is high, in a case where the necessity for acquiring the second image is high.

A non-transitory computer-readable recording medium stores therein an extraction program that causes a computer to execute a process including: generating a plurality of combinations of conditions relating to a plurality of item values included in data; calculating an index value that indicates a degree of cooccurrence between a specified response variable and each of the plurality of combinations, by using a machine learning model that estimates a response variable from the plurality of item values, the machine learning model having been trained by using the data; and extracting a specific combination from among the plurality of combinations based on any one of the condition and the index value.

An image detection method determines a target object. A plurality of original images of a scene in front of a vehicle are obtained. An object in one original image is detected, and a degree of similarity between the object in the original image and the target object in a preset image is calculated. If the degree of similarity is greater than a preset similarity threshold, it is determined that the original image is a target image and the object is the target object. A position of the target object relative to the vehicle is determined and output. The method can recognize objects of interest in front of a driver.

A non-transitory computer-readable recording medium stores an information processing program for causing a computer to execute a process including: extracting a first feature from an image; detecting, from the extracted first feature, a plurality of visual entities included in the image; generating a second feature in which the visual entities in at least one combination of the plurality of detected visual entities are combined, in first feature, with each other; generating, based on the first feature and the second feature, a first map that indicates relation of each visual entity; extracting a fourth feature based on the first map and a third feature obtained by converting the first feature; and estimating the relation from the fourth feature.

The present invention discloses a method of detecting an abusing act in an online service, the method including: a use act information collecting operation in which an apparatus for detecting an abusing act collects use act information including the kinds and an order of one or more use acts performed by a user; an image configuring operation in which the kinds of the one or more use acts performed by the user are arranged in a time order and an image is configured by setting the kinds of predetermined use acts to a first axis and setting the order of the use acts to a second axis; and an abusing act detecting operation in which whether the use act of the user is an abusing act is detected by using the image.

A system, method and computer-readable medium are provided to enable digital bank endorsement. A digital image of a back side of a check may be placed in a computer memory. Appropriate coordinates for a bank endorsement may be determined. A bank endorsement may be automatically generated. The digital image may then be electronically altered by overlaying, merging, or rendering text of the generated bank endorsement. A modified digital image may be combined with an image of the front side of the check and stored and/or exported to check clearing operations.

Implementations are described herein for analyzing a sequence of digital images captured by a mobile vision sensor (e.g., integral with a robot), in conjunction with information (e.g., ground truth) known about movement of the vision sensor, to determine spatial dimensions of object(s) and/or an area captured in a field of view of the mobile vision sensor. Techniques avoid the use of visual indicia of known dimensions and/or other conventional tools for determining spatial dimensions, such as checkerboards. Instead, techniques described herein allow spatial dimensions to be determined using less resources, and are more scalable than conventional techniques.

A technique for improving the computational time for performing a non-maximum suppression operation may include receiving a request to perform a non-maximum suppression operation on a set of candidate predictions of a computing task, and performing a statistical analysis on a set of confidence scores corresponding to the set of candidate predictions to determine a standard deviation of the set of confidence scores. A confidence score threshold can be determined based on the standard deviation. Candidate predictions having a confidence score below the confidence score threshold can then be discarded to form a reduced set of candidate predictions. Additional candidate predictions can be discarded from the reduced set of candidate predictions based on an intersection-over-union overlap metric, and the remaining candidate predictions from the reduced set of candidate predictions can be provided as a result of the non-maximum suppression operation.

A system for initiating a command of an electric vertical take-off and landing (eVTOL) aircraft includes a flight controller configured to receive a topographical datum, identify an air position as a function of a sensor and the topographical datum, wherein identifying further comprises obtaining a sensor datum as a function of the sensor, and identifying the air position as a function of the sensor datum and the topographical datum using a similarity function, determine a command as a function of the air position, and initiate the command.

In an apparatus for identifying an obstacle around a vehicle, an acquirer is configured to acquire an image captured by a camera mounted to the vehicle. An extractor is configured to extract feature points of the image. A generator is configured to generate an optical flow that is a movement vector from each of the feature points of the image acquired before the current time to a corresponding feature point of the image acquired at the current time. A classifier configured to classify the optical flows into groups each corresponding to an object in the image based on pixel positions of the feature points. An identifier is configured to, for each of the groups that the optical flows are classified by the classifier into, identify whether an object corresponding to the group in the image is a stationary object or a moving object based on a degree of variability in lengths of the optical flows of the group.