Abstract: In an example, based upon a first image of a face of a first person, a plurality of augmented images may be generated. Based upon the first image and the plurality of augmented images, a first set of facial feature representations may be generated. A second image comprising a representation of a face of a second person may be identified. A second facial feature representation may be generated based upon the second image. It may be determined, based upon the second facial feature representation and the first set of facial feature representations, that the second person is the first person.

Abstract: In one aspect, an information processing apparatus includes a first acquisition module, a first extraction module, a first generation module, a second extraction module, a derivation module, and a first output control module. The first acquisition module acquires an input image to output a first image and a second image. The first extraction module extracts first characteristic point information from the first image. The first generation module generates a third image obtained by reducing a data amount of the second image. The second extraction module extracts second characteristic point information from the third image. The derivation module derives a difference between the first characteristic point information and the second characteristic point information. The first output control module outputs the third image corrected in accordance with the difference as an output image.

Abstract: Objects within an image are assigned a semantic interest that is indicative of their importance to the image as a whole. The objects are detected automatically, and sub-images that span each of the objects are excerpted from the image. An image embedding is determined for each of the sub-images as well as for the whole image by using an image encoder implemented as a trained multi-modal neural network. The degree of similarity between the image embeddings of a sub-image and that of the whole image is used as a measure of the semantic importance of the object portrayed in the sub-image. Objects of high semantic importance comprise the semantic regions of interest of the image. Knowledge of such regions may be used to enhance and improve the efficiency of downstream image-processing tasks, such as image compression, pan and scan, and contrast enhancement.

Abstract: There is provided an image processing apparatus. An obtainment unit obtains a first image, first subject information indicating a first subject detected from the first image, a second image, and second subject information indicating a second subject detected from the second image. A composition unit generates a composite image by compositing the first image and the second image. A recording unit records one of the first subject information and the second subject information in association with the composite image in a case where a similarity between the first subject information and the second subject information satisfies a predetermined criterion, and records both of the first subject information and the second subject information in association with the composite image in a case where the similarity between the first subject information and the second subject information does not satisfy the predetermined criterion.

Abstract: There is provided a method and a system configured obtain an image of a semiconductor specimen including one or more arrays, each including repetitive structural elements, and one or more regions, each region at least partially surrounding a corresponding array and including features different from the repetitive structural elements, wherein the PMC is configured to, during run-time scanning of the semiconductor specimen, perform a correlation analysis between pixel intensity of the image and pixel intensity of a reference image informative of at least one of the repetitive structural elements, to obtain a correlation matrix, use the correlation matrix to distinguish between one or more first areas of the image corresponding to the one or more arrays and one or more second areas of the image corresponding the one or more regions, and output data informative of the one or more first areas of the image.

Abstract: Presented herein are systems and methods for the employment of machine learning models for image processing. A method may include a capture of a video feed including image data of a document at a client device. The client device can provide the video feed to another computing device. The method can include, by the client device or the other computing device object recognition for recognizing a type of document and capturing an image exceeding a quality threshold of the document amongst the frames within the video feed. The method may further include the execution of other image processing operations on the image data to improve the quality of the image or features extracted therefrom. The method may further include anti-fraud detection or scoring operations to determine an amount of risk associated with the image data.

Abstract: A power consumption control device applied to an electronic device includes an image signal processor (ISP), a storage device, a processing circuit, and a control circuit. The ISP is arranged to receive an image signal captured by a camera of the electronic device, and process the image signal to generate a processed image signal. The storage device is arranged to store at least one predetermined image class. The processing circuit is arranged to analyze the processed image signal to detect whether the processed image signal belongs to the at least one predetermined image class to generate a control signal. The control circuit is arranged to switch a mode of the electronic device to a first mode or a second mode according to the control signal, wherein power consumption and performance of the electronic device in the first mode are lower than that in the second mode.

Abstract: Embodiments of the present disclosure relate to a calibration verification method for a camera intrinsic parameter, an apparatus, a device, and a medium. The method includes: acquiring a plurality of reference images photographed by a target camera with a plurality of reference world coordinates under a preset photographing condition, wherein the camera intrinsic parameter of the target camera is calibrated; determining, a first coordinate change parameter corresponding to the plurality of reference world coordinates according to the preset photographing condition, and determining a second coordinate change parameter of the plurality of reference images according to a preset algorithm; determining whether the second coordinate change parameter is consistent with the first coordinate change parameter; and if the second coordinate change parameter is consistent with the first coordinate change parameter, feeding back a verification message indicating successful calibration of the camera intrinsic parameter.

Abstract: A method for estimating ripeness of produce includes illuminating the produce with light, measuring intensities of the light reflected from the produce at different frequencies, and determining a degree of ripeness of the produce from the relative intensities of the light reflected from the produce at the different frequencies.

Abstract: The present application concerns the visual identification of materials or documents for tracking or authentication purposes. It describes methods to automatically authenticate an object by comparing some object images with reference images, the object images being characterized by the fact that visual elements used for comparison are non-disturbing for the naked eye. In some described approaches it provides the operator with visible features to locate the area to be imaged. It also proposes ways for real-time implementation enabling user friendly detection using mobile devices like smart phones.

Abstract: A method, computer system, and a computer program product for historical image search is provided. The present invention may include receiving an image search criteria. The present invention may also include generating a composite image search query based on the received image search criteria. The present invention may further include searching for a matching historical image based on the generated composite image search query. The present invention may also include returning a historical image search result.

Abstract: Provided herein are systems and methods for extracting text from a document. Different optical character recognition (OCR) tools are used to extract different versions of the text in the document. Metrics evaluating the quality of the extracted text are compared to identify and select higher quality extracted text. A selected portion of text is compared to a threshold to ensure minimal quality. The selected portion of text is then saved. Error correction can be applied to the selected portion of text based on errors specific to the OCR tools or the document contents.

Abstract: Technologies are provided herein for adaptively learning and matching modalities of face images. A face image is obtained and a biometric signature is generated of the face image. The biometric signature is compared with a stored biometric signature obtained from data storage. Differences are determined between the biometric signature and the stored biometric signature and compared with a proximity threshold. As a result of the comparison with the proximity threshold, the identity of the person in the face image may be determined or the face image may be stored in data storage in association with the identity of the person.

Abstract: Multimodal techniques are described for classifying start pages and/or document types of an unstructured document image package. To that end, some implementations of the disclosure relate to a method, including: obtaining a document image file including multiple pages and multiple document types; generating, for each page of the multiple pages, using multiple independent trained models, multiple independent predictions, each of the multiple independent predictions indicating: whether or not the page is a first page of a document, or a document type of the multiple document types that the page corresponds to; and generating, for each page of the multiple pages, based on the multiple independent predictions, using a neural network, a final prediction output indicating whether or not the page is the first page of a document, or one of the multiple document types that the page corresponds to.

Abstract: A device receives user interface interaction data that identifies an interaction that a user has with an interface that displays an image of a vehicle. The device generates, by using a data model to process at least a portion of the image, an array of vectors that includes one or more vectors relating to vehicle characteristics of the vehicle. The device assigns one or more weights to the one or more vectors based on the user interface action data. The device determines, based on a similarity analysis, similarity scores that indicate similarities between the array of vectors that include the one or more vectors that have been weighted and other arrays relating to the vehicles depicted in the images. The device selects a subset of the images based on the similarity scores and causes the subset of the images to be displayed.

Abstract: A method of annotating known objects in road images captured from a sensor-equipped vehicle, the method implemented in an annotation system and comprising: receiving at the annotation system a road image containing a view of a known object; receiving ego localization data, as computed in a map frame of reference, via localization applied to sensor data captured by the sensor-equipped vehicle, the ego localization data indicating an image capture pose of the road image in the map frame of reference; determining, from a predetermined road map, an object location of the known object in the map frame of reference, the predetermined road map representing a road layout the map frame of reference, wherein the known object is one of: a piece of road structure, and an object on or adjacent a road; computing, in an image plane defined by the image capture pose, an object projection, by projecting an object model of the known object from the object location into the image plane; and storing, in an image database, image

Abstract: A method for processing an image includes first obtaining a media image. The media image has a target scene object at least partially shielded. A target shielding object is determined from the media image. The target shielding object at least partially shields the target scene object. Image inpainting is performed on a first region of the media image corresponding to the target shielding object to generate a target media image.

Abstract: A method of identifying a defensive alignment and an offensive alignment in a set-piece is disclosed herein. A computing system receives one or more streams of tracking data. The computing system identifies a set-piece contained in the one or more streams of tracking data. The computing system identifies a defensive alignment of a first team and an offensive alignment of a second team. The computing system extracts, via a convolutional neural network, one or more features corresponding to a type of defensive alignment implemented by the first team by passing the set-piece through the convolutional neural network. The computing system scans the set-piece, via a machine learning algorithm, to identify one or more features indicative of a type of offensive alignment implemented by the second team. The computing system infers the type of defensive alignment implemented by the first team.

Abstract: The present disclosure describes systems and techniques for creating a super-resolution image (122) of a scene captured by a user device (102). Natural handheld motion (110) introduces, across multiple frames (204, 206, 208) of an image of a scene, sub-pixel offsets that enable the use of super-resolution computations (210) to form color planes (212, 214, 216), which are accumulated (218) and combined (220) to create a super-resolution image (122) of the scene.

Abstract: A connected component analysis (CCA) method, which can use labels repeatedly, comprising: defining a label pattern comprising a label and a plurality of neighboring labels; setting a center label of a current pixel of a target binary image according to a binary value of the current pixel and the neighboring pixels; setting at least two of the neighboring labels according to whether the current pixel is in any one of a first row, a first column and a last column; and recording the center label to a label buffer. Labels for marking pixels of the target binary image are first center labels, and then are second center labels, and are the first center labels again after the labels are the second center labels.

Abstract: One embodiment provides a method comprising acquiring 3D content comprising a 3D object in 3D space. The 3D object has object information indicative of a location of the 3D object in the 3D space. The method further comprises projecting the 3D object to a 2D object in 2D space based on the object information. The 2D object has one or more 2D vertices indicative of a location of the 2D object in the 2D space. The method further comprises determining one or more latent variables in the 2D space based on the object information and the one or more 2D vertices, and generating a 2D feature database including the one or more latent variables.

Abstract: A processor-implemented method with pose estimation includes: tracking a position of a feature point extracted from image information comprising a plurality of image frames, the image information being received from an image sensor; predicting a current state variable of an estimation model for determining a pose of an electronic device, based on motion information received from a motion sensor; determining noise due to an uncertainty of the estimation model based on a residual between a first position of the feature point extracted from the image frames and a second position of the feature point predicted based on the current state variable; updating the current state variable based on the current state variable, the tracked position of the feature point, and the noise; and determining the pose of the electronic device based on the updated current state variable.

Abstract: An apparatus and method to adjust item recommendations are disclosed herein. A first image attribute of a query image is compared to a second image attribute of each of a plurality of inventory images of a plurality of inventory items to identify the inventory items similar to the query image. Item recommendations comprising the identified inventory items in a first listing order are provided for display at a remote device. A second listing order of the identified inventory items is determined based on a user preference for a particular one of the identified inventory items. At least the second listing order is provided to the remote device for re-display of the item recommendations in accordance with the second listing order.

Abstract: A video camera comprises a housing (102) configured of a top housing portion (104), a mid-housing portion (106) and a bottom housing portion (108). The top housing portion is configured as a circular and rounded exterior contoured wall (110) having an exterior recessed feature (112) formed along an edge (114). The mid-housing portion (106) is formed of an IR window ring (116) having an alignment tab (118) extending therefrom, the alignment tab being aligned and recessed within the exterior recessed feature (112) of the rounded exterior contoured wall (110) of the top housing portion (104), and the bottom housing portion (108) providing a camera dome (120) seated within and protruding from the IR window ring (116).

Abstract: The present system and method relate generally to the field of Robotic Process Automation, particularly to a form data extractor for document processing. The system and method relate to a form extractor for document processing using RPA workflows that can be easily configured for different document types. The form extractor includes a set of templates for identifying the document type (classification) and extracting data from the documents. The templates can be configured, i.e., by the user, by defining the fields to be extracted and the position of the field on the document. The form extractor is resilient to changes in the position of the template on a page, as well as to scan rotation, size, quality, skew angle variations and file formats, thus allowing RPA processes to extract data from documents that need ingestion, independent of how they are created.

Abstract: The invention provides a technique capable of effectively and appropriately removing noise from various kinds of images including noise and artifacts and images in which a noise pattern changes due to a difference in imaging conditions. Based on a noise removal technique using AI, noise characteristics including artifacts are analyzed for each image, the image is classified based on an analysis result, an optimal neural network for a noise processing is applied for each classification, and the noise and the artifacts are reduced.

Abstract: Systems, methods, and data storage devices for image grouping in an end user device using trained machine learning group classifiers are described. The end user device may include an image group classifier configured to classify new image data objects using an image classification algorithm and set of machine learning parameters previously trained for a specific image group. The end user device may determine embeddings that quantify features of the target image object and use those embeddings and the image group classifier to selectively associate group identifiers with each new image data object received or generated by the end user device. Calibration, including selection and training, of the image group classifiers and ranking of classified images are also described.

Abstract: The disclosure relates to AI-based machine-learning and natural language modeling to identify semantic similarities between sets of content having natural language text. For example, a system may generate a relevance classification that indicates whether content such as articles are non-specifically relevant to charities without identifying a particular charity. If the content is non-specifically relevant to charities, the system may apply a natural language model to generate sentence embeddings based on the content and determine a level similarity between the sentence embeddings and a query embedding generated from a charity query. The charity query may itself be generated from a full description of the charity through an encoder-decoder architecture with reinforcement learning.

Abstract: A game image generation unit 112 generates a moving image of content. A receiving unit 104 receives a displayed image changing request. Upon reception of the changing request by the receiving unit 104, a display control unit 106 acquires information regarding a frame image of the content from the frame buffer 108. A system image generation unit 120 uses the information regarding the frame image to generate an image that differs from the frame image. The display control unit 106 acquires information regarding an end frame image of the content or an image preceding the end frame image by a predetermined number of frames.

Abstract: An image differentiation system receives input feature vectors for multiple input images and reference feature vectors for multiple reference images. In some cases, the feature vectors are extracted by an image feature extraction module trained based on training image triplets. A differentiability scoring module determines a differentiability score for each input image based on a distance between the input feature vectors and the reference feature vectors. The distance for each reference feature vector is modified by a weighting factor based on interaction metrics associated with the corresponding reference image. In some cases, an input image is identified as a differentiated image based on the corresponding differentiability score. Additionally or alternatively, an image modification module determines an image modification that increases the differentiability score of the input image. The image modification module generates a recommended image by applying the image modification to the input image.

Abstract: The present disclosure relates to a color classification system that accurately classifies objects in digital images based on color. In particular, in one or more embodiments, the color classification system utilizes a multidimensional color space and one or more color mappings to match objects to colors. Indeed, the color classification system can accurately and efficiently detect the color of an object utilizing one or more color similarity regions generated in the multidimensional color space.

Abstract: A method of providing Internet commercialization services to a content owner may include obtaining rights from an owner of Web page contents on a Web page to commercialize the Web page contents, obtaining an ad from an advertiser for placement on the Web page, the advertiser agreeing to pay for placement of the ad, paying for traffic to the Web page based on a bid to a traffic partner and sharing profits with the owner of the Web page contents, if any, from payments by the advertiser above payment to the traffic partner for the traffic. The content owner may be a publisher of content on the Internet. The traffic partner may be a search engine. The method may include automatically providing Internet commercialization services to an Internet publisher. Various aspects of the method may be modified in a closed loop fashion to optimize profits.

Abstract: A method includes receiving 2D image data corresponding to a 2D image of a human face of a subject. The method further includes determining a textual identifier that describes a facial feature of the human face represented by the 2D image. The method further includes providing, to a first generative machine learning model, a first prompt including information identifying the textual identifier that describes the facial feature of the human face. The method further includes obtaining, from the first generative machine learning model, a first output identifying, among a plurality of sets of false eyelashes, a set of false eyelashes selected to suit the human face of the subject based on the facial feature.

Abstract: Systems and techniques are provided for adjusting objects in images. For example, a process can include obtaining a first image of a scene from a camera. The scene can include a first object positioned at a first position and a second object positioned at a second position. The process can include obtaining a second image of the scene from the camera. The second image of the scene can include the first object positioned at the first position and the second object positioned at a third position different from the second position. The process can include generating an adjusted second image based on the second image. The adjusted second image can include the first object positioned at the first position. The second object at the third position is removed from the adjusted second image. The process can include displaying the adjusted second image on a display.

Abstract: An image processing method, an image processing apparatus, and an image processing system are provided. In the method, a target object is determined in an original image to generate a labeling result. The labeling result includes a position of the target object in the original image. Multiple target images of the target object are generated according to the labeling result. The target images are generated by extracting image of the target object from the original image and changing an image size of the image of the target object. A corresponding target image is combined with the original image according to a zoom operation. The zoom operation is configured to change an image size for displaying the original image.

Abstract: A method for classifying a glass object via acoustic analysis by a classifying apparatus is provided. The method including: receiving, by a processor, sound data of a knock sound generated by applying a knocking operation on the glass object; determining, by the processor, a type of the glass object by performing a knock-sound analysis to the sound data, wherein the type of the glass object includes an organic glass and an inorganic glass; if the type of the glass object is determined as the inorganic glass, receiving, by the processor, echo data of an echo induced by applying an ultrasonic-echo operation on the glass object; and determining, by the processor, a further type of the glass object by performing an echo-decay analysis to the echo data, wherein the further type of the glass object includes a crystal glass, a borosilicate glass and a soda-lime glass.

Abstract: An inspection apparatus selects at least one character area, in a first preview image obtained by reading and previewing a print product, sets a direction, for a character in the selected character area, registers the set direction and the character in the selected character area in association with each other, selects at least one character inspection area, in a second preview image obtained by reading and previewing a print product as an inspection target, sets a direction, for a character in the selected character inspection area, rotates the character inspection area to match the set direction, with the direction set for the character in the selected character area, performs character recognition, for the character in the rotated character inspection area, and inspects the character inspection area, based on a result of the character recognition and a result of recognizing the character in the selected character area.

Abstract: Embodiments of the present disclosure provide methods, apparatus, systems, and computer program products for using an image of an integrated circuit (IC) including a plurality of cells to locate one or more target cells within the IC. Accordingly, in various embodiments, a footprint for each cell of the plurality of cells is encoded to transform the image of the IC into a two-dimensional string matrix. A string search algorithm is then applied on each encoded dopant region found in the two-dimensional string matrix using an encoded target layout cell to identify one or more candidate regions of interest within the image. Finally, a mask window is slid over each candidate region of interest while performing matching using match criteria to identify any target cells in the one or more target cells that are located within the candidate region of interest.

Abstract: The present application concerns the visual identification of materials or documents for tracking or authentication purposes. It describes methods to automatically authenticate an object by comparing some object images with reference images, the object images being characterized by the fact that visual elements used for comparison are non-disturbing for the naked eye. In some described approaches it provides the operator with visible features to locate the area to be imaged. It also proposes ways for real-time implementation enabling user friendly detection using mobile devices like smart phones.

Abstract: A redundant tracking system comprising multiple redundant tracking sub-systems, enabling seamless transitions between such tracking sub-systems, provides a solution to this problem by merging multiple tracking approaches into a single tracking system. This system is able to combine tracking objects with six degrees of freedom (6 DoF) and 3 DoF through combining and transitioning between multiple tracking systems based on the availability of tracking indicia tracked by the tracking systems. Thus, as the indicia tracked by any one tracking system becomes unavailable, the redundant tracking system seamlessly switches between tracking in 6 DoF and 3 DoF thereby providing the user with an uninterrupted experience.

Abstract: In an example, a plurality of images of an interface of an application may be identified. An image of the plurality of images may be compared with a second image of the plurality of images to determine a first set of regions, of the interface, in which the second image is different than the first image. Based upon the first set of regions, a set of dynamic regions of the interface may be determined. A fourth image of the interface may be identified. Based upon the set of dynamic regions, a first portion of the fourth image may be compared with a first portion of the first image to determine user interface testing information associated with the interface of the application.

Abstract: Embodiments of the present disclosure relate to a method, an electronic device, and a computer program product for data processing. The method for data processing includes: generating, based on a category of a target data content segment, a target tag for the target data content segment; acquiring a reference tag set, a reference tag in the reference tag set being generated based on a category of a historical data content segment; and determining redundancy of the target data content segment based on comparison between the target tag and the reference tag set. In this way, the distribution of a large amount of redundant data from an edge server to a terminal device can be avoided, thereby saving bandwidth, improving delay, and further providing a better user experience for AR/VR content experience.

Abstract: A method may include obtaining a facial image of a subject and identifying a number of new images to be synthesized with target AU combinations and categories of intensity. The method may also include synthesizing the number of new images using the facial image of the subject as the base image with the number of target AU combinations and categories of intensity with a number of new images that have different AU combinations than the facial image of the subject. The method may additionally include adding the number of new images to a dataset and training a machine learning system using the dataset to identify a facial expression of the subject.

Abstract: In a digital image annotation and retrieval system, a machine learning model identifies an image feature in an image and generates a plurality of question prompts for the feature. For a particular feature, a feature annotation is generated, which can include capturing a narrative, determining a plurality of narrative units, and mapping a particular narrative unit to the identified image feature. An enriched image is generated using the generated feature annotation. The enriched image includes searchable metadata comprising the feature annotation and the plurality of question prompts.

Abstract: Disclosed are methods and systems, among which is a system that includes a pattern-recognition processor, a central processing unit (CPU) coupled to the pattern-recognition processor via a pattern-recognition bus, and memory coupled to the CPU via a memory bus. In some embodiments, the pattern-recognition bus and the memory bus form about the same number of connections to the pattern-recognition processor and the memory, respectively.

Abstract: A sortation system is disclosed that includes a programmable motion device including an end effector, a perception system for recognizing any of the identity, location, and orientation of an object presented in a plurality of objects, a grasp selection system for selecting a grasp location on the object, the grasp location being chosen to provide a secure grasp of the object by the end effector to permit the object to be moved from the plurality of objects to one of a plurality of destination locations, and a motion planning system for providing a motion path for the transport of the object when grasped by the end effector from the plurality of objects to the one of the plurality of destination locations, wherein the motion path is chosen to provide a path from the plurality of objects to the one of the plurality of destination locations.

Abstract: The present disclosure relates to a method for automatically generating labeled data of a hand, comprising: acquiring at least three images to be processed of the hand under different angles of view; detecting key points on the at least three images to be processed respectively; screening the detected key points by using an association relation among the at least three images to be processed, the association relation being the same frame of image of the at least three images to be processed from the hand under different angles of view; reconstructing a three-dimensional space representation of the hand with regard to the key points screened on the same frame of image, in combination with a given finger bone length; projecting the key points on the three-dimensional representation of the hand onto the at least three images to be processed; and generating the labeled data of the hand on the images to be processed by using the projected key points on the at least three images to be processed.

Abstract: A method may include providing one or more inputs to a hybrid data generator, wherein one of the one or more inputs is based at least in part on a wellsite location, wherein the hybrid data generator comprises a large language model, and wherein the large language model is based at least in part on a machine learning algorithm. The method may further include utilizing an information handling system to generate a drilling program based at least in part on the one or more inputs and the hybrid data generator. The method may further include performing at least a portion of a drilling operation based at least in part on the drilling program and collecting at least one measurement from at least one sensor during the drilling operation.

Abstract: Disclosed herein are system, method, and computer program product embodiments for surface automation in black box environments. An embodiment operates by determining scenarios of an application for automation; detecting the scenario during an execution of an application; capturing and storing one or more user interface screenshots of the scenario; identifying and storing user interface information from the user interface screenshot; implementing a sequential set of instructions comprising at least one textual element detection technique and at least one non-textual element detection technique; and executing the sequential set of instructions.

Abstract: A system may identify a defined rectangular picture area and render video corresponding to the defined rectangular picture area. The system may receive a video bitstream comprising a picture having a header and may receive data specifying a structure of the picture. The system may parse the data specifying the structure of the picture for an identifier corresponding to a defined rectangular area in the first picture and for a tile index of a top left tile in the defined rectangular area. The system may determine one or more tiles comprised in the defined rectangular area based on the identifier corresponding to the defined rectangular area and the tile index of the top left tile. The system may reconstruct the picture including a sub-picture that comprises the defined rectangular area based upon the identifier corresponding to the defined rectangular area. The computing system may render the sub-picture in the defined rectangular area.