Abstract: The present disclosure relates to a video retrieval method, a method, system and device for generating a video retrieval mapping relationship, and a storage medium. The video retrieval method comprises: acquiring a retrieval instruction, wherein the retrieval instruction carries retrieval information for retrieving a target frame picture; and obtaining the target frame picture according to the retrieval information and a preset mapping relationship.

Abstract: A method and an apparatus for generating a video fingerprint are disclosed. The method includes: performing shot boundary detection on content of a video; determining a time duration of each shot according to positional points of the shot boundary, and compose the time duration of each shot into a shot boundary time slice sequence; and obtaining video fingerprint information according to the time slice sequence.

Abstract: A method for estimating a pose of a deformable object includes: receiving, by a processor, a plurality of images depicting the deformable object from multiple viewpoints; computing, by the processor, one or more object-level correspondences and a class of the deformable object depicted in the images; loading, by the processor, a 3-D model corresponding to the class of the deformable object; aligning, by the processor, the 3-D model to the deformable object depicted in the plurality of images to compute a six-degree of freedom (6-DoF) pose of the object; and outputting, by the processor, the 3-D model and the 6-DoF pose of the object.

Abstract: An automated teller machine comprising a sensor, a network interface, and a processor configured to receive, during a transaction at the automated teller machine, user data from the sensor configured to monitor user movement information during the transaction, receive, via the network interface, user data from a mobile device, determine a risk of a fraudulent transaction based on a comparison of the user movement information from the sensor and the user data from the mobile device to expected parameters, determine that the risk of a fraudulent transaction is below a threshold, and allow access to a secure resource at the automated teller machine.

Abstract: Systems, devices, and methods are disclosed for generating an authenticity score for a creative work using digital image data of one or more creative works. A system receives, via a network, first digital image data of a first creative work. The system determines from the first digital image data, a first set of feature variables, each corresponding to a characteristic of the first creative work. The system determines, via an artificial intelligence model and with input including the first set of feature variables and a comparison dataset, an authenticity score for the first creative work. The artificial intelligence model generates, based on the portion of first feature variables of the first creative work that match corresponding portions of the comparison dataset, an output indicative of an authenticity score. The system communicates, via the network, an authenticity score of the first creative work to a client device.

Abstract: An image-processing device that includes at least one processor which is configured to: set discrete measurement regions in a standard image and a reference image selected from images acquired in a time series manner and calculate motion vectors in the respective measurement regions; set a region of interest in the standard image; select, from the calculated motion vectors, the motion vectors in the set region of interest; estimate a homographic-transformation matrix that represents a motion in the region of interest by using the selected motion vectors; evaluate an error in the homographic-transformation matrix on the basis of the estimated homographic-transformation matrix and the calculated motion vectors; and set, on the basis of the evaluation result for the homographic-transformation matrix estimated on the basis of a first region of interest, a second region of interest a size of which is increased as compared with that of the first region of interest.

Abstract: There is described a computer-implemented method and system for classifying images, the computer-implemented method comprising: receiving an image to be classified, generating a vector representation of the image to be classified using an image embedding method, comparing the vector representation of the image to predefined vector representations of the predefined image categories, and identifying a relevant category amongst the predefined image categories based on the comparison, the relevant category being associated with the image to be classified and outputting the relevant category.

Abstract: A system and method for classification. In some embodiments, the method includes forming a first training dataset and a second training dataset from a labeled input dataset; training a first classifier with the first training dataset; training a variational auto encoder with the second training dataset, the variational auto encoder comprising an encoder and a decoder; generating a third dataset, by feeding pseudorandom vectors into the decoder; labeling the third dataset, using the first classifier, to form a third training dataset; forming a fourth training dataset based on the third dataset; and training a second classifier with the fourth training dataset.

Abstract: A position and pose determination unit (103) is configured to determine a position and an pose of the video acquisition unit (110) in such a way that a level of hiding caused by overlapping of objects becomes low based on environmental object information indicating an environmental object including a structural object present in the monitored area and a placed object placed in the structural object, and staying characteristic information indicating a staying characteristic of the object determined depending on the environmental object. The analysis and display unit (111) is configured to perform at least one of analyzing a video captured by the video acquisition unit (103) at the position and the pose determined by the position and pose determination unit (110) and displaying the video.

Abstract: The invention provides a computer-implemented method of processing an image, the method comprising the steps of: identifying objects in the image using a content analysis engine; based on information from a relationship database, using a contextual analysis engine to automatically: identify contextual relationships between some or all of the identified objects in the image, derive a background or landscape of said image from one or more of said identified objects; and subsequently, and analyse the identified contextual relationships between the identified objects and the derived background or landscape to thereby associate one or more contexts with said image.

Abstract: A first set of pixels forming a first set of rows and a first set of columns of a first image acquired by an image acquisition device is received. A first corrected set of pixels forming a first corrected set of rows and a first corrected set of columns is generated from the first set of pixels. Successive pixels of each row from the first corrected set of rows correspond to successive parallel lines in a plane defined by the sheet of light that are equally spaced along a first axis of a world coordinate system. Successive pixels of each column from the first corrected set of columns correspond to successive parallel lines in the plane defined by the sheet of light that are equally spaced along a second axis of the world coordinate system. Based on the first corrected set of pixels, a first set of points of the sheet of light is determined. The first set of points of the sheet of light is used to obtain a three-dimensional profile of the object.

Abstract: An unmanned aerial vehicle (UAV) includes a propulsion system, a global navigation satellite system (GNSS) sensor, a camera and a controller. The controller includes logic that, in response to execution by the controller, causes the UAV to in response to detecting a loss of tracking by the GNSS sensor determine an estimated location of the UAV on a map based on a location image captured by the camera, determine a route to a destination using tracking parameters embedded in the map, wherein the map is divided into a plurality of sections and the tracking parameters indicate an ease of determining a location of the UAV using images captured by the camera with respect to each section, and control the propulsion system to cause the UAV to follow the route to the destination.

Abstract: An image processing pipeline including a still or video camera includes a first neural network arranged to process and provide a neural network based result for at least one of an image capture setting, sensor processing, global post processing, local post processing, and portfolio post processing. A second neural network is arranged to receive the neural network result and further provide at least one of an image capture setting, sensor processing, global post processing, local post processing, and portfolio post processing, wherein at least one of the first and second neural networks generate data on local processors supported by the still or video camera.

Abstract: Systems and methods are provided for identifying and recommending electronic content to consumers. In accordance with an implementation, one or more elements of electronic content are identified based on video graph data. In an exemplary method, information associated with a first element of video content is received, and corresponding video graph data is obtained. One or more second elements of video content that are similar to the first element of video content are identified based on the obtained video graph data. A subset the first and second elements of video content is subsequently identified for delivery to the user.

Abstract: A computer-implemented method for searching for a similar image in an image database includes receiving a query image, computing a value of a similarity measure of the query image, comparing the value of the similarity measure of the query image with metadata of the partitions, discarding at least one partition of the image database based on the comparison of the value of the similarity measure of the query image with the metadata, comparing the images of at least one of the remaining partitions with the query image, and selecting at least one image of the at least one partition of the remaining partitions as the similar image based on the comparison of the images of the at least one of the remaining partitions with the query image.

Abstract: Systems and methods are provided for receiving a plurality of images corresponding to a listing in an online marketplace, generating a scene type for each image of the plurality of images, and grouping each image into a scene type group of a set of predefined scene types. Each group of images are input into a respective machine learning model specific to the scene type of the group of images to generate a visual score for each image in each group of images, and an attractiveness score is generated for the listing in the online marketplace based on the visual scores for each image in each group of images.

Abstract: A method for generating an order includes acquiring by one or more processors, an initial image and a first detection result of a target container before being subjected to a pick-up operation, where the first detection result includes a first detection image with a number of first commodity frames obtained by processing the initial image, and first identification information corresponding to a commodity in each of the first commodity frames, acquiring by one or more processors, a result image of the target container after performing the pick-up operation, generating by one or more processors, an RGB difference image between before-shopping and after-shopping, based on the result image and the initial image, where the RGB difference image is obtained by calculating an absolute value of differences of individual pixels of the initial image and the result image before and after shopping in three RGB channels.

Abstract: An autonomous mobile device (AMD) follows a user who may move among other people, change orientation, or move in and out view of a camera on the AMD. A predicted location of the user may be determined based on a previous location and previous movement. A proximity value is determined by the distance between a location in physical space of a user and the predicted location. An image from a camera is processed to determine a user depicted in the image and generate a feature vector (FV) of that depiction. A gallery of FVs of the user as viewed from different angles, poses, and so forth is stored. A similarity value is determined between a FV of an unidentified user in an image and the FVs in the gallery. The user may be identified using the proximity value and the similarity value.

Abstract: A biometric electronic signature authenticated key exchange (“BESAKE”) token processing system. The system includes a storage location having a plurality of biometric reference templates. The system further includes an authentication computing system having a processor and instructions. The instructions configured to cause the authentication computing system to receive a signing party identifier and the BESAKE token from a signing party. The BESAKE token having a biometric sample encrypted using an encryption key. The instructions further configured to generate a decryption key and decrypt the encrypted biometric sample from the BESAKE token. The instructions further configured to match the biometric sample with a biometric reference template and transmit to a biometric service provider computing system a match request. The instructions further configured to determine a signing party identity via a binary match value.

Abstract: Techniques for masking images based on a particular task are described. A system masks portions of an image that are not relevant to a particular task, thus, reducing the amount of data used by applications for image processing tasks. For example, images to be processed using a hair color classification model are masked so that only portions that show the person's hair are available for the model to analyze. The system configures different masker components to mask images for different tasks. A masker component can be implemented at a user device to mask images prior to sending to an application/task-specific model.

Abstract: Uncertainty-based data filtering in a vehicle may include acquiring sensor data from a plurality of sensors of the autonomous vehicle; applying a first machine learning model to the sensor data; determining that a result of the first machine learning model disagrees with another result; and transmitting, based on the result of the first machine learning model disagreeing with the other result, the sensor data to a server.

Abstract: A feedback processing module includes a memory configured to store feedback received from an encoder. The feedback includes parameters associated with encoded graphics content generated by a graphics engine. The feedback processing module also includes a processor configured to generate configuration information for the graphics engine based on the feedback. The graphics engine is configured to execute a workload based on the configuration information. In some cases, the feedback processing module is also configured to receive feedback from a decoder that is used to decode the graphics content that is encoded by the encoder and generate the configuration information based on the feedback received from the decoder.

Abstract: Features are extracted and tabulated for transactions on a per-operator basis. Metrics are derived from the features on a per-feature basis and on an aggregate bases for the features as a whole. Additional metrics are calculated on a per-feature basis and an aggregate bases for a group of operators. A current transaction being conducted in real time by a given operator has current features extracted and a risk score is generated. The risk score is compared against the metrics associated with the given operator and the group of operators. A determination is made as to whether the current transaction is fraud or is not fraud based on the comparison. When fraud is detected, an alert is raised, and the current transaction provided to a fraud system for further fraud evaluation.

Abstract: The disclosed embodiments describe methods, systems, and apparatuses for determining user intent. A method is disclosed comprising obtaining a session text of a user; calculating, by the processor, a feature vector based on the session text; determining probabilities that the session text belongs to a plurality of intent labels, the probabilities calculated using a multi-level hierarchal intent classification model, the intent labels assigned to levels in the multi-level hierarchal intent classification model; and assigning a user intent to the session text based on the probabilities.

Abstract: A method for image segmentation includes receiving an input image. The method further includes obtaining a deep learning model having a triad of predictors. Furthermore, the method includes processing the input image by a shape model in the triad of predictors to generate a segmented shape image. Moreover, the method includes presenting the segmented shape image via a display unit.

Abstract: Disclosed are a system and method for quantifying changes in animal posture from video recordings of multiple interacting animals. Also disclosed are a system and method for training a posture identification algorithm efficiently with minimal human effort and evaluating the posture of millions of animal images. The disclosed process employs video recordings of behaving animals to produce a full set of geometrical coordinates for each body part over time. Inter alia, the disclosed system should be useful for organizations interested in doing large small-molecule screens to find drugs that affect the behavior of animals, and for those investigating new behavioral diagnostics for human patients. The disclosed system allows one to record video of animals and then automatically calculate the position of each body part over time. The output of this calculation can be used for simple analysis of the amount of movement for specific body parts, or part of a more complex scheme for measuring behaviors.

Abstract: Obtain, from an existing machine learning classifier, original probabilistic scores classifying samples taken from two or more groups into two or more classes via supervised machine learning. Associate the original probabilistic scores with a plurality of original Lagrange multipliers. Adjust values of the plurality of original Lagrange multipliers via low-dimensional convex optimization to obtain updated Lagrange multipliers that satisfy fairness constraints as compared to the original Lagrange multipliers. Based on the updated Lagrange multipliers, closed-form transform the original probabilistic scores into transformed probabilistic scores that satisfy the fairness constraints while minimizing loss in utility. The fairness constraints are with respect to the two or more groups.

Abstract: A multispectral sensor device may include a sensor array comprising a plurality of channels and one or more processors to determine that a time-sensitive measurement is to be performed, wherein the time-sensitive measurement is to be performed using data collected by one or more channels of the plurality of channels; cause the data to be collected by a proper subset of channels, of the plurality of channels, wherein the proper subset of channels includes the one or more channels; and determine the time-sensitive measurement based on the data.

Abstract: Apparatus, methods, and computer-readable media for providing frameworks for data source representation and compression using an index file format are disclosed herein. The index file format separate information about symbols in a data file and information about the corresponding location of those symbols in the data file. The described techniques provide mechanisms for reducing the size associated with the representation of the symbols information and/or the size associated with the representation of the location information.

Abstract: Method and apparatus for mechanical sex-sorting of mosquitoes by extracting a class of mosquitoes from unsorted mosquitoes comprises obtaining unsorted mosquitoes, obtaining images of individual mosquitoes in a stationary phase, electronically classifying the individuals from the images into male mosquitoes and/or female mosquitoes, and possibly also unclassified objects; obtaining co-ordinates of individuals of at least one of the male mosquito and female mosquito classifications, and using a robot arm to reach an individual identified by the obtained coordinates to store or remove the individuals, thereby to provide sex-sorted mosquitoes.

Abstract: The present description relates to systems, methods, and non-transitory computer readable media for generating digital responses to digital queries by utilizing a classification model and query-specific analysis models. For example, the described systems can train a classification model to generate query classifications corresponding to product queries, conversational queries, and/or recommendation/purchase queries. Moreover, the described systems can apply the classification model to select pertinent models for particular queries. For example, upon classifying a product query, the described systems can utilize a neural ranking model (trained based on a set of training product specifications and training queries) to generate relevance scores for product specifications associated with a digital query.

Abstract: A metadata extraction machine accesses an image that depicts an item. The item depicted in the image may have an attribute that describes a characteristic of the item and an attribute descriptor that corresponds to the attribute of the item and specifies a value of the attribute. The metadata extraction machine performs an analysis of the image. The analysis may include identifying the attribute descriptor corresponding to the attribute based on image segmentation of the image. The metadata extraction machine transmits a communication to a device of a user based on the identifying of the attribute descriptor corresponding to the attribute of the item depicted in the image.

Abstract: Systems and methods are disclosed for model based document image enhancement. Instead of requiring paired dirty and clean images for training a model to clean document images (which may cause privacy concerns), two models are trained on the unpaired images such that only the dirty images are accessed or only the clean images are accessed at one time. One model is a first implicit model to translate the dirty images from a source space to a latent space, and the other model is a second implicit model to translate the images from the latent space to clean images in a target space. The second implicit model is trained based on translating electronic document images in the target space to the latent space. In some implementations, the implicit models are diffusion models, such as denoising diffusion implicit models based on solving ordinary differential equations.

Abstract: A diagnostic tool for deep learning similarity models and image classifiers provides valuable insight into neural network decision-making. A disclosed solution generates a saliency map by: receiving a baseline image and a test image; determining, with a convolutional neural network (CNN), a first similarity between the baseline image and the test image; based on at least determining the first similarity, determining, for the test image, a first activation map for at least one CNN layer; based on at least determining the first similarity, determining, for the test image, a first gradient map for the at least one CNN layer; and generating a first saliency map as an element-wise function of the first activation map and the first gradient map. Some examples further determine a region of interest (ROI) in the first saliency map, cropping the test image to an area corresponding to the ROI, and determine a refined similarity score.

Abstract: This disclosure relates generally to categorical time-series clustering. In an embodiment, the method for categorical time-series clustering for categorical time-series associated with distinct subjects obtained from sensors. Based on the categorical time-series, the subjects are clustered into clusters by using a Markov chain model. Clustering the subjects include assigning each subject to a cluster. The subjects are assigned to the clusters by determining cluster-specific transition matrices based on a transitional probability of the subject's transitioning between states. A semi-distance function is constructed for each cluster-specific transitional matrix between the states at multiple time instances, which us indicative of a conditional probability of movement of the subject between the states at different time instance.

Abstract: A product defect detection method, device and system are disclosed.

Abstract: This invention relates to a method of blocking or passing messages sent via a firewall, based on parsing, of symbols strings contained in said messages, among different keywords, assigning to said messages either a blocking class or a passing class, comprising: performing a dimensionality reduction step for said different classes on a training set of messages whose classes are known, and then classifying one or more unknown messages among said different classes with reduced dimensionality, said dimensionality reduction step being performed on said training set of messages by machine learning including processing, for several first matrices and for several second matrices, a parameter representative of a product of two first and second matrices to assess to which given class a given message belongs: first matrix representing a first array of keywords versus symbols strings contained in a first given message, second matrix representing the values of differences between said first array and a second array of key

Abstract: Disclosed herein are system, method, and computer program product embodiments for distinguishing a disease state from a non-disease state in an image. An embodiment operates by receiving an image of a target area over a network. The embodiment then corrects for background noise in the image by applying a semantic segmentation filter to obtain a segmented image. The sematic segmentation filter may be trained to remove the background noise from the image. The embodiment then determines, using a trained artificial intelligence (AI) model and the segmented image, at least one classification for the target area. The embodiment finally causes the display of the at least one classification and disease information on a user device associated with a user. The trained AI model may be trained using at least augmented images obtained from a set of images to correct for at least an imbalance in the set of images.

Abstract: Computer software development has produced many advances within computer science and in most aspects of modern society. Even with modern quality control, bug finding, and other code checking applications, computer software is often less than ideal. A developer may write code that is functionally accurate but lacks security, documentation, speed, storage, reusability, or other element that may make a segment of software code less than ideal. Identifying equivalent code, within a defined hypothesis strength and/or resource limitation, and, when found, replacing it with a vetted equivalent promotes the deployment of software that is more robust, secure, usable and reusable, and/or satisfies performance or other objectives.

Abstract: A method of improving image quality of a stream of input images is described. The stream of input images, including a current input image, is received. One or more target objects, including a first target object, are identified spatio-temporally within the stream of input images. The one or more target objects are tracked spatio-temporally within the stream of input images. The current input image is segmented into i) a foreground including the first target object, and ii) a background. The foreground is processed to have improved image quality in the current input image. Processing of the foreground further comprises processing the first target object using a same processing technique as for a prior input image of the stream of input images based on the tracking of the first target object. The background is processed differently from the foreground. An output image is generated by merging the foreground with the background.

Abstract: Techniques for determining a probability of a false negative associated with a location of an environment are discussed herein. Data from a sensor, such as a radar sensor, can be received that includes point cloud data, which includes first and second data points. The first data point has a first attribute and the second data point has a second attribute. A difference between the first and second attributes is determined such that a frequency distribution may be determined. The frequency distribution may then be used to determine a distribution function, which allows for the determination of a resolution function that is associated with the sensor. The resolution function may then be used to determine a probability of a false negative at a location in an environment. The probability can be used to control a vehicle in a safe and reliable manner.

Abstract: Provided are a processing method, a terminal device, and a medium. The method includes the steps described below. Basic information of a target video is determined; attribute information of the target video is determined based on the basic information; in a case where the attribute information indicates that the target video is a video capable of being structured, chapter division is performed on the target video based on the basic information to obtain at least two video clips; and chapter description information of the at least two video clips, a key frame of the at least two video clips and video description information of the target video are determined.

Abstract: A method of reducing false positives and identifying relevant true alerts in a video management system includes analyzing images to look for patterns indicating changes between subsequent images. When a pattern indicating changes between subsequent images is found, the video management system solicits from a user an indication of whether the pattern belongs to one of two or more predefined categories. The patterns indicating changes between subsequent images are saved for subsequent use. Subsequent images received from the video camera are analyzed to look for patterns indicating changes between subsequent images. When a pattern indicating changes between subsequent images is detected by the video management system, the video management system compares the pattern indicating changes between subsequent images to those previously categorized into one of the two or more predefined categories. Based on the comparison, the video management system may provide an alert to the user.

Abstract: Systems and methods for vehicle-to-vehicle communications are provided. An example computer-implemented method includes obtaining from a first autonomous vehicle, by a second autonomous vehicle, a first compressed intermediate environmental representation. The first compressed intermediate environmental representation is indicative of at least a portion of an environment of the second autonomous vehicle. The method includes generating a first decompressed intermediate environmental representation by decompressing the first compressed intermediate environmental representation. The method includes determining, using one or more machine-learned models, an updated intermediate environmental representation based at least in part on the first decompressed intermediate environmental representation and a second intermediate environmental representation generated by the second autonomous vehicle.

Abstract: Methods, apparatuses, and non-transitory machine-readable media for displaying information and/or images on a display of a computing device based on received data. Apparatuses can include a display screen, a memory resource, a recognition sensor, and a controller. An example controller can receive data and activate information and/or images on a display screen based in part on the received data. In another example, a method can include storing recognition data in a memory resource, receiving primary recognition data, comparing the primary recognition data to the stored recognition data, and activating the display screen for a viewing angle responsive to authentication of the primary recognition data through the comparison of the primary recognition data and the stored recognition data.

Abstract: A dual sensor imaging system and a privacy protection imaging method thereof are provided. The system is configured to control at least one color sensor and at least one IR sensor to respectively capture multiple color images and multiple IR images by adopting multiple exposure conditions adapted for an imaging scene, adaptively select a combination of the color image and the IR image that can reveal details of the imaging scene, detect a feature area with features of a target of interest in the color image, and fuse the color image and the IR image to generate a fusion image with the details of the imaging scene, and crop an image of the feature area of the fusion image to be replaced with an image not belonging to the IR image, so as to generate a scene image.

Abstract: Systems and methods for trajectory prediction and motion device control include receiving a first set of data about a surrounding environment of a motion device. The first set of data includes first sensory data and first position data. The method includes receiving a second set of data about the surrounding environment of the motion device. The second set of data includes second sensory data and second position data, where the first sensory data differs in modality from the second sensory data. The method also includes extracting features from the first set of data that model motion behavior of the motion device, and extracting features from the second set of data that model motion behavior of the motion device. The method includes inputting the features into a machine learning model, and computing a first trajectory based on a first latent variable and a second trajectory based on a second latent variable.

Abstract: Provided are an AI system for simulating functions such as recognition, determination, and so forth of human brains by using a mechanical learning algorithm like deep learning, or the like, and an application thereof. In particular, according to the AI system and the application thereof, a neural network training method includes obtaining a plurality of first images belonging to a particular category and a plurality of second images for which a category is not specified, training a neural network model for category recognition, based on the plurality of first images belonging to the particular category, recognizing at least one second image corresponding to the particular category among the plurality of second images, by using the trained neural network model, and modifying and refining the trained neural network model based on the recognized at least one second image.

Abstract: A method, a computer system, and a computer program product are provided for federated learning. An aggregator may receive cluster information from distributed computing devices. The cluster information may relate to identified clusters in sample data of the distributed computing devices. The cluster information may include centroid information per cluster. The aggregator may include a processor. The aggregator may integrate the cluster information to define data classes for machine learning classification. The integrating may include computing a respective distance between centroids of the clusters in order to determine a total number of the data classes. The aggregator may send a deep learning model that includes an output layer that has a total number of nodes equal to the total number of the data classes. The deep learning model may be for the distributed computing devices to perform machine learning classification in federated learning.

Abstract: A system for viewing at a client device a series of three-dimensional virtual views over the Internet of a volume visualization dataset contained on centralized databases employs a transmitter for securely sending volume visualization dataset from a remote location to the centralized database, more than one central data storage medium containing the volume visualization dataset, and a plurality of servers in communication with the centralized databases to create virtual views based on client requests. A resource manager load balances the servers, a security device controls communications between the client device and server and the resource manager and central storage medium. Physically secured sites house the components. A web application accepts at the remote location user requests for a virtual view of the volume visualization dataset, transmits the request to the servers, receives the resulting virtual view from the servers, and displays the resulting virtual view to the remote user.