An artificial intelligence apparatus for recognizing an object included in image data can include a camera, a communication modem, a memory configured to store an image recognition model, a natural language processing (NLP) model, and an NLP model-based image recognition model learned based on the NLP model, and a processor is configured to receive image data from the camera or the communication modem, in response to recognizing an object included in the image data using the image recognition model, generate first recognition information on the object included in the image data, and in response to the recognizing the object included in the image data using the image recognition model being unsuccessful, generate second recognition information on the object included in the image data based on recognizing the object using the NLP model-based image recognition model.

A method and an apparatus for labeling data are provided. The method may include: acquiring to-be-labeled data and a labeling requirement for the to-be-labeled data; determining a labeling method type meeting the labeling requirement, where the labeling method type is a labeling method type used for the to-be-labeled data in order to meet the labeling requirement; generating a labeling title matching the labeling method type according to the labeling requirement, where the labeling title is used to prompt a labeling content in a labeling tool; and generating the labeling tool including the to-be-labeled data, the labeling title and a title logical relationship. The present disclosure can determine labeling method types for different to-be-labeled data and labeling requirements.

An information processing apparatus includes a processor configured to: with multiple pieces of form data having attributes of forms being grouped in accordance with form definition information that defines groups respectively with the attributes, receive a change of an attribute of an ungrouped piece of the form data; and re-group the form data in accordance with an attribute responsive to the change and the form definition information.

An information processing apparatus includes a processor configured to display data in forms to be processed, switchably in one of a first mode, in which the data in the forms are displayed for each type of the forms, and a second mode, in which the data in the forms are displayed for each processing unit by which the data in the forms are to be processed.

An information processing apparatus includes a processor configured to receive, if an error is found in sorting of a form image after an operator checks and corrects a result of character recognition performed on the sorted form image, an instruction to cause a process to revert to the sorting for the form image.

A gas detection device, a gas detection method, and a gas detection program according to the present invention detect gas on the basis of an image obtained by imaging a detection target. The gas detection device, the gas detection method, and the gas detection program; generated accumulated data obtained by accumulating a number of times of gas detection in a predetermined unit of accumulation, on the basis of a plurality of images captured at a plurality of times different from each other in a predetermined periond; and generate a mask image for suppressing notification of detected gas on the basis of the generated accumulated data.

The present invention provides a matching result display device, a matching result display method, a program, and a recording medium, which allow, at the time of displaying feature points of objects determined as matching on the basis of matching result information provided by a matching system, visual check of the displayed feature points to be performed easily. The matching result display device according to the present invention includes: a feature point acquiring unit that acquires a plurality of feature points common to at least two pieces of image data to be matched against each other; a display position setting unit that sets display positions of corresponding symbols to be assigned to the feature points common to the at least two pieces of image data in such a manner that, when the same corresponding symbols are assigned to corresponding feature points, respectively, a display line connecting each feature point and the corresponding symbol assigned thereto on each of the at least two piece of image data does not overlap with display lines connecting other feature points and corresponding symbols assigned thereto, respectively; and a display unit that provides the corresponding symbols at the display positions and displays the feature points and the corresponding symbols assigned thereto in a state where they are connected with display lines, respectively.

In some examples, a processor may receive images from a camera mounted on a vehicle. The processor may generate a disparity image based on features in at least one of the images. In addition, the processor may determine at least one region in a first image of the received images that has a brightness that exceeds a brightness threshold. Further, the processor may determine at least one region in the disparity image having a level of disparity information below a disparity information threshold. The processor may determine a region of light interference based on an overlap between at least one region in the first image and at least one region in the disparity image, and may perform at least one action based on the region of light interference.

A method and an active commissioning system for video analytics (VA) of video surveillance are provided. The method includes capturing, using a video capture device, a video sequence and receiving, at a VA processor, a parameter set from a cloud resource, wherein the parameter set is used for video analytics. The method also includes processing the video sequence by selecting subparts of the video sequence as events, wherein the events are detected based on the parameter set, generating a visualized report using the events from the VA processor and providing the visualized report to an operator, marking, by the operator, yes/no on the events in the visualized report, generating annotations for events based on the marking, and transmitting the annotations and the events to the cloud resource for processing, wherein the cloud resource processes the annotations and events and provides the parameter set based on the processing.

A fingerprinting solution that uses neural network (NN) based trained Machine Learning (ML) modules in combination with traditional image processing for contactless fingerprint capture, liveness detection to rule out fake fingers, and fingerprint matching using a portable handheld device with integrated camera, thereby eliminating the need for a special device dedicated for fingerprinting. The trained NN modules detect the size and direction of fingers in the captured image, check if fingers are reversed in the image (thereby making nails visible), check if the thumb of the correct hand is captured, and generate fixed-length fingerprint templates for subsequent matching of fingerprints. Three dimensional (3D) depth map of a finger is used to bring the fingerprint resolution to 500 dpi and eliminate distortion caused by the curvature of the finger shape to improve accuracy while scaling and flattening a fingerprint image. The solution facilitates contactless-to-contactless as well as contactless-to-contact based fingerprint matching.