Abstract: A method for constructing an image includes: defining a foreground area associated with an object in an original image; identifying a plurality of contour points defining a contour of the object; for each of the contour points, obtaining a reference contour point set that includes at least one reference contour point on each of two sides of the contour point; obtaining a plurality of characteristic lines, each associated with the reference contour point set and defined by an end point obtained from the contour points; and aligning the end points on one side to form a straight edge and making the characteristic lines adjoin each other side by side, so as to construct a reconstructed image.

Abstract: Embodiments of the present disclosure provide a method and apparatus for optimizing a tag of a point of interest. The method includes: acquiring a set of points of interest and a set of tags of points of interest in the set of points of interest; generating a point of interest-tag matrix based on the set of points of interest and the set of tags of points of interest in the set of points of interest; extracting a feature of a point of interest-tag in the point of interest-tag matrix; inputting the feature of the point of interest-tag in the point of interest-tag matrix into a pre-trained ternary self-adaptive collaborative learning model, to obtain a point of interest-tag score matrix; and optimizing the set of tags of points of interest in the set of points of interest based on the point of interest-tag score matrix.

Abstract: A method of identifying and retrieving component digital images for component fault analysis includes generating a known-fault database of digital images of known faults of a previously analyzed component and corresponding remedial actions. The method also includes accessing the known-fault database with a digital image of a current fault of a new component. The method additionally includes comparing the digital image of the current fault with the digital images in the known-fault database based on a computed target characteristic. The method also includes sorting the digital images in the known-fault database in order based on a magnitude of the computed target characteristic for each respective digital image in the known-fault database relative to the digital image of the current fault. The method further includes outputting the sorted digital images to facilitate correlation of the current fault to a particular known fault and identifying the corresponding remedial action.

Abstract: Embodiments relate to a system, comprising, an electromechanical platform for positioning and orienting a collectible to capture a plurality of images by an image capturing device of at least a first side and a second side of the collectible, a computer comprising at least one processor comprising computer-executable instructions stored on one or more computer-readable media, wherein the computer is operable to receive the plurality of images of the collectible, at least one processing routine comprising an image processing algorithm for a condition assessment of the collectible applied to at least one image from the plurality of images by at least one processor to obtain a raw data of the condition of the collectible, a device for encapsulating the collectible in a tamper proof casing and stamping at least one or more labels on the tamper proof casing, wherein the stamping of the at least one or more labels is at a specified location on the tamper proof casing; and wherein the system is operable to be fully

Abstract: In one embodiment, a method includes accessing a plurality of verified videos depicting one or more subjects, generating, based on the verified videos, a plurality of verified-video feature values corresponding to the subjects, generating, using one or more video transformations based on the verified videos, a plurality of fake videos, generating, based on the fake videos, a plurality of fake-video feature values, training a machine-learning model to determine whether a specified video is a genuine video, wherein the machine-learning model is trained based on the verified-video feature values and the fake-video feature values. The machine-learning model may be trained to classify the specified video in a genuine-video class or a fake-video class, and the machine-learning model maybe trained based on an association between a genuine-video class and the verified-video feature values and an association between a fake-video class and the fake-video feature values.

Abstract: Methods and systems for determining one or more alignment parameters for use in a process performed on a specimen are provided. One method includes determining measures of similarity between images generated by an imaging system for corresponding locations in each of two or more pairs of dies on a specimen and performing cluster analysis based on the determined measures of similarity to identify the images that are most similar to each other and to assign different subsets of the images that are most similar to each other to different die clusters, respectively. The method also includes separately determining one or more alignment parameters for two or more of the different die clusters. The one or more alignment parameters are used for aligning images generated by the imaging system for the specimen or another specimen to a common reference.

Abstract: A method for manufacturing a product is provided. The method includes: providing a plurality of components; working on the plurality of components to form the product; and testing the product via a testing method, including: providing first images and second images; assigning the first images by an assigner to a first inspecting unit; determining whether the product corresponding to the first images is OK or not in the first inspecting unit; additionally assigning the second image by the assigner to the first inspecting unit in case no more first image is to be assigned; determining whether the product corresponding to the second image is OK or not in the second inspecting unit or additionally in the first inspecting unit; and sending a message of OK by the assigner for outputting the product that is determined OK.

Abstract: Embodiments for intelligent interpretation of image processing results using machine learning in a computing environment by a processor. One or more data sets may be transformed into one or more pseudo-image representations to enable one or more image processing tasks for image processing. An interpretation of an image processing task result from applying the one or more image processing tasks on the one or more pseudo-image representations generated from one or more data sets.

Abstract: A method of quantifying wear data on a well tool includes identifying the well tool based on a solid model file and a data file corresponding to the well tool, wherein the solid model and data files are stored on a computer system and separately identify wear parts of the well tool, scanning the well tool with a scanner and thereby generating a scanned file of the well tool, the scanner being in communication with the computer system, aligning the scanned file with the solid model file to obtain an overlay output, creating digital features on the wear parts of the scanned file, and calculating deviation between the solid model file and the scanned file at the digital features for the wear parts and thereby determining material removed from the wear parts of the well tool.

Abstract: A system and method for determining a mood for a crowd is disclosed. In example embodiments, a method includes identifying an event that includes two or more attendees, receiving at least one indicator representing emotions of attendees, determining a numerical value for each of the indicators, and aggregating the numerical values to determine an aggregate mood of the attendees of the event.

Abstract: An obstacle classification method and apparatus based on unmanned vehicle, a device, and a storage medium are provided. The method includes: obtaining obstacle information of a plurality of obstacles detected by a detection device of the unmanned vehicle; and performing classification processing on the obstacle information using a compressed random forest model, to obtain a result of obstacle classification, where a non-leaf node of each decision tree of the compressed random forest model stores only feature index information, classification threshold index information and node position index information, the node position index information is left or right node position index information; a leaf node of each decision tree stores only class index information. Only part of information needs to be stored, and there is no need to occupy more memory and space of the unmanned vehicle system, thereby reducing the memory occupancy, and improving the speed and efficiency of classification.

Abstract: This disclosure describes a system for utilizing multiple image processing techniques to identify an item represented in an image. In some implementations, one or more image processing algorithms may be utilized to process a received image to generate item image information and compare the item image information with stored item image information to identify the item. When a similarity score identifying the similarity between the item image information and at least one of the stored item image information is returned, a determination may be made as to whether the similarity score is high enough to confidently identify the item. If it is determined that the similarity score is high enough to confidently identify the item, the other algorithms may be terminated and the determined identity of the item returned.

Abstract: This document relates to performing video analytics on a cloud device that preserves privacy. One example uses data-oblivious algorithms to process input video data, where the data-oblivious algorithms can assist with preventing attackers from exploiting side-channels induced by data-dependent access patterns.

Abstract: This invention classifies actions performed within a video clip, by receiving a video clip for analysis, where the video clip comprises a time sequence of video frames, applies a bottleneck attention mechanism to the frames in the clip to generate a reduced sequence of key-frames, applies a 2 dimensional (2D) convolutional neural network to the sequence of keyframes to obtain a 3D embedding tensor for each keyframe, applies a multi-headed attention mechanism to the 3D embedding tensors to generate a final action context, and apples a classification mechanism to the final action context to obtain a probability for each action class that indicates the likelihood that an action specified by the action class occurred in the video clip.

Abstract: Methods for matching features in patterns for electronic designs include inputting a set of pattern data for semiconductor or flat panel displays, where the set of pattern data comprises a plurality of features. Each feature in the plurality of features is classified, where the classifying is based on a geometrical context defined by shapes in a region. The classifying uses machine learning techniques.

Abstract: A distance-based score approximation having improved computational efficiency is provided. Responsive to receiving a score request, a computing entity identifies an observation point based on a location indicated in the score request and defines a set of annuli comprising a plurality of concentric annuli centered on the observation point and defined by a predetermined maximum radius. The computing entity queries a geographic database for map information corresponding to geometry elements located within the predetermined maximum radius of the observation point and determines an intersection of each geometry element with each annulus. The computing entity determines a contribution for each intersection based at least in part on a size of the intersection, a measure assigned to the corresponding geometry element, and a representative radius of the corresponding annulus.

Abstract: A system is disclosed, in accordance with one or more embodiment of the present disclosure. The system may include a controller including one or more processors configured to execute a set of program instructions. The set of program instructions may be configured to cause the processors to: receive images of a sample from a characterization sub-system; identify target clips from patch clips; prepare processed clips based on the target clips; generate encoded images by transforming the processed clips; sort the encoded images into a set of clusters; display sorted images from the set of clusters; receive labels for the displayed sorted images; determine whether the received labels are sufficient to train a deep learning classifier; and upon determining the received labels are sufficient to train the deep learning classifier, train the deep learning classifier via the displayed sorted images and the received labels.

Abstract: Methods and systems for identifying events of interest in images from imagers in an edge computing network, including: detecting a movement based on image processing in images captured by the imager, cropping an area of the at least one image where the movement is detected, applying a first machine learning (ML) algorithm having a deep neural network architecture to classify an object of interest in the cropped area of the at least one image, sending the at least one image to a server, when the first ML algorithm classifies the object of interest, applying a second ML algorithm having a deep neural network architecture to detect the object of interest in the at least one image where the object of interest is detected based on context analysis.

Abstract: The present invention relates to the determination of damage to portions of a vehicle. More particularly, the present invention relates to determining whether determined damage to a vehicle is consistent with information provided as to the cause of the damage to the vehicle. Aspects and/or embodiments seek to provide a computer-implemented method for determining whether damage to a vehicle, which is determined using images of the damage to the vehicle, is consistent with information documenting the cause of the damage to the vehicle, for example insurance claim data or repair shop proposed repair data.

Abstract: A method for inspecting images on print products in a printing material processing machine includes recording and digitizing produced print products using at least one image sensor in an image inspection process of a print job using an image inspection system. The recorded digital prints that have been created are compared to a digital reference image by using a computer and, if deviations are found between the recorded digital prints and the digital reference image, print products found to be defective are removed. The image sensor records the produced print products from at least two different viewing angles and/or viewing distances and the computer evaluates the at least two recorded digital prints as a function of the at least two viewing angles and/or viewing distances and assesses them in terms of deviations. A device for inspecting images on print products is also provided.