Image analytics systems, methods, and computer program products to autonomously analyze an image to identify and detect features in the image, such as the horizon, and/or identify and detect objects of interest therein, such as, smoke or possible smoke. The image is captured, for example, by RGB cameras, and depicts a scene to be analyzed. The intelligent image analytic system is configured to provide alerts and/or other information to one or more concerned parties and/or computing systems to take an appropriate response.

A method for acquiring at least one two-dimensional image of a part of arches of a patient includes steps carried out by the patient or other person who is not a dental health professional, for example, including placing a dental separator in the mouth of the patient in order to separate the lips of the patient and improve the visibility of the teeth during the acquisition of said at least one two-dimensional image, and acquiring, in a mouth closed position and with a personal image acquisition apparatus, said at least one two-dimensional image.

This disclosure describes techniques for parallel fingerprint capturing and matching, thereby enabling large-area or high-resolution fingerprint identification with low latency. Rather than waiting to capture an entire fingerprint image (“a verify image”), a fingerprint identification process divides the verify image into blocks and attempts to match the blocks to corresponding portions of an enrolled image even as other portions are being. Rather than waiting to capture and analyze the entire fingerprint image at once, small groups of blocks are captured and the already-captured blocks are matched and scored to corresponding blocks of an enrolled image, in some cases, while additional blocks of the verify image are being captured. A cumulative score and cumulative confidence in the overall matching of the enrolled image is derived from the scores and confidences of the individual block scores and the verify image is authenticated based on each satisfying their respective thresholds.

Systems, computer program products, and methods are described herein for preserving image and acoustic sensitivity using reinforcement learning. The present invention is configured to initiate a file editing engine on the audiovisual file to separate the audiovisual file into a video component and an audio component; initiate a convolutional neural network (CNN) algorithm on the video component to identify one or more sensitive portions in the one or more image frames; initiate an audio word2vec algorithm on the audio component to identify one or more sensitive portions in the audio component; initiate a masking algorithm on the one or more image frames and the audio component; generate a masked video component and a masked audio component based on at least implementing the masking action policy; and bind, using the file editing engine, the masked video component and the masked audio component to generate a masked audiovisual file.

A method and a platform of generating a document, an electronic device, and a storage medium are provided, which relate to a field of an artificial intelligence technology, in particular to fields of computer vision and deep learning technologies, and may be applied to a text recognition scenario and other scenarios. The method includes: performing a category recognition on a document picture to obtain a target category result; determining a target structured model matched with the target category result; and performing, by using the target structured model, a structure recognition on the document picture to obtain a structure recognition result, so as to generate an electronic document based on the structure recognition result, wherein the structure recognition result includes a field attribute recognition result and a field position recognition result.

An individual object identification system includes: an image acquisition processor configured to perform an image acquisition process to acquire an image of a subject acquired using imaging equipment; a feature point extraction processor that extracts a feature point from the image; a local feature amount calculation processor that calculates a local feature amount of the feature point; a local feature amount group classification processor that performs classification into a predetermined number of local feature amount groups; a global feature amount calculation processor that calculates a global feature amount based on each of the local feature amount groups; a searching target image registration processor that registers a plurality of images that are searching targets; a global feature amount registration processor that registers the global feature amount related to each of the registered images in a global feature amount registration unit; a narrowing processor that narrows down the plurality of registered images to registered images each having the global feature amount highly correlated with a global feature amount of an identification image; and a determination processor that compares the registered images as candidates with the identification image to determine the registered image having the largest number of corresponding points.

A part inspection system includes a vision device configured to image a part being inspected and generate a digital image of the part. The system includes a part inspection module communicatively coupled to the vision device and receives the digital image of the part as an input image. The part inspection module includes a defect detection model. The defect detection model includes a template image. The defect detection model compares the input image to the template image to identify defects. The defect detection model generates an output image. The defect detection model configured to overlay defect identifiers on the output image at the identified defect locations, if any.

Aspects of the subject disclosure may include, for example, a camera positioned to capture image information of an immersive experience presented to one or more users engaged in the immersive experience and located in an immersive experience space, a processing system and a memory that stores executable instructions to facilitate performance of operations including receiving the image information from the camera, detecting objects located in the immersive experience space with the one or more users, the objects including at least one virtual object created by the immersive experience, determining the at least one virtual object is a projected virtual object of the immersive experience, generating a signal indicating the at least one virtual object is a projected virtual object, and a projector, responsive to the signal, to provide a visual indication in the immersive experience space to identify the projected virtual object as a virtual object to the one or more users engaged in the immersive experience. Other embodiments are disclosed.

Examples provide a self-supervised language model for document-to-document similarity scoring and ranking long documents of arbitrary length in an absence of similarity labels. In a first stage of a two-staged hierarchical scoring, a sentence similarity matrix is created for each paragraph in the candidate document. A sentence similarity score is calculated based on the sentence similarity matrix. In the second stage, a paragraph similarity matrix is constructed based on aggregated sentence similarity scores associated with the first candidate document. A total similarity score for the document is calculated based on the normalize the paragraph similarity matrix for each candidate document in a collection of documents. The model is trained using a masked language model and intra-and-inter document sampling. The documents are ranked based on the similarity scores for the documents.

An information processing apparatus includes a frame image acquisition section adapted to acquire a plurality of consecutive frame images included in a moving image displayed on a screen, and a matching process section adapted to perform, for each of the plurality of acquired frame images, a matching process of detecting an area that matches a template image representing appearance of a display element to be detected. An area in which the display element is being displayed on the screen is identified on a basis of a result of performing the matching process on the plurality of frame images.