Abstract: A liveness detection system comprises a controller, a video input, a feature recognition module, and a liveness detection module. The controller is configured to control an output device to provide randomized outputs to an entity over an interval of time. The video input is configured to receive a moving image of the entity captured by a camera over the interval of time. The feature recognition module is configured to process the moving image to detect at least one human feature of the entity. The liveness detection module is configured to compare with the randomized outputs a behaviour exhibited by the detected human feature over the interval of time to determine whether the behaviour is an expected reaction to the randomized outputs, thereby determining whether the entity is a living being.

Abstract: Techniques for generating 3D gaze predictions based on a deep learning system are described. In an example, the deep learning system includes a neural network. The neural network is trained with training images. During the training, calibration parameters are initialized and input to the neural network, and are updated through the training. Accordingly, the network parameters of the neural network are updated based in part on the calibration parameters. Upon completion of the training, the neural network is calibrated for a user. This calibration includes initializing and inputting the calibration parameters along with calibration images showing an eye of the user to the neural network. The calibration includes updating the calibration parameters without changing the network parameters by minimizing the loss function of the neural network based on the calibration images. Upon completion of the calibration, the neural network is used to generate 3D gaze information for the user.

Abstract: Quality judgment on a laser beam intensity distribution is performed by taking an observation condition of the laser beam into consideration. A machine learning device includes: a state observing means that acquires data indicating an intensity distribution of a laser beam and data indicating a condition for observing the laser beam, performed to generate the data indicating the intensity distribution as input data; a label acquisition means that acquires an evaluation value related to judgment of the quality of the laser beam as a label; and a learning means that performs supervised learning using a pair of the input data acquired by the state observing means and the label acquired by the label acquisition means as training data to construct a learning model for judging the quality of the laser beam.

Abstract: A system, method, and computer program product for producing a consistent desired set of operational parameters during use of a patterning solution that would otherwise alter an initial set of operational parameters. A variable match process is adjusted dynamically to counter changes to the performance metric to tend to maintain the performance metric at a desired predetermined specification.

Abstract: The present disclosure provides a method and apparatus for infrared thermal image contour extraction. The method includes: obtaining an infrared thermal image; and determining histograms based on grayscale image of the infrared thermal image, determining segmentation thresholds of the infrared image based on the histograms, and extracting a contour based on the segmentation thresholds. The apparatus includes an image acquisition circuit, configured to capture an infrared thermal image; and a processing circuit, configured to determine histograms based on greyscale images of the infrared thermal image, to determine segmentation thresholds of the infrared thermal image based on the histograms, and to extract a contour based on segmentation thresholds.

Abstract: According to a first aspect of the present disclosure, a fingerprint authentication system is provided, comprising: a transformation unit configured to transform a first format of a captured fingerprint into a second format of the captured fingerprint, wherein the first format defines coordinates of minutia positions and the second format defines relative positions of minutiae; and an authentication unit configured to compare the relative positions with stored reference values. According to a second aspect of the present disclosure, a corresponding fingerprint authentication method is conceived. According to a third aspect of the present disclosure, a corresponding computer program product is provided.

Abstract: A method and system for performing automated defect detection is disclosed. The system may include at least one database, an image capture device and a processor. The method may comprise providing at least one database for storing information used in processing data to detect a defect in at least one member of a plurality of members in a device. The information may include a plurality of different modes of data. The method may further comprise providing a processing unit for processing the information; receiving, by the database, updates to the information; identifying a potential defect in a first mode of data; applying, by the processing unit, analysis of a second mode of data, the analysis of the second mode of data triggered by the identifying, the second mode of data different than the first mode of data; and reporting defects based on the results of the applying.

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: Quality judgment on a laser beam intensity distribution is performed by taking an observation condition of the laser beam into consideration. A machine learning device includes: a state observing means that acquires data indicating an intensity distribution of a laser beam and data indicating a condition for observing the laser beam, performed to generate the data indicating the intensity distribution as input data; a label acquisition means that acquires an evaluation value related to judgment of the quality of the laser beam as a label; and a learning means that performs supervised learning using a pair of the input data acquired by the state observing means and the label acquired by the label acquisition means as training data to construct a learning model for judging the quality of the laser beam.

Abstract: Systems, methods, and articles of manufacture to perform an operation comprising processing, by a machine learning (ML) algorithm and a ML model, a plurality of images in a first dataset, wherein the ML model was generated based on a plurality of images in a training dataset, receiving user input reviewing a respective set of tags applied to each image in the first data set as a result of the processing, identifying, based on a first confusion matrix generated based on the user input and the sets of tags applied to the images in the first data set, a first labeling error in the training dataset, determining a type of the first labeling error based on a second confusion matrix, and modifying the training dataset based on the determined type of the first labeling error.

Abstract: A coding method including dividing pixels of a chrominance component of an input image into blocks having a predetermined size; selecting one among a direct current prediction method, a vertical prediction method, a horizontal prediction method, and a hybrid prediction method according to a user's input; generating a prediction value of each pixel in a current block to be predictively coded, using at least one pixel value among pixel values in an upper reference block adjacent to the current block and in a side reference block adjacent to the current block, according to the selected prediction method; generating a differential value between the prediction value and a corresponding real pixel value in the current block; and coding the differential value and information on the selected prediction method using a predetermined coding method.

Abstract: A method of generating, by means of a processing device, a depth map and/or a fully focused image of a scene, from a series of images of the scene taken under a same viewing angle but with a different focusing, the method including the steps of: a) for each position of a pixel in the series of images, searching for an extremum of the pixel value in the series of images; and b1) assigning to said pixel position a depth value which is a function of the rank, in the series of images, of the image where the extremum has been identified at step a) to construct a depth map of the scene, and/or b2) assigning to said pixel position the value of the extremum identified at step a) to construct a fully focused image of the scene.

Abstract: An image group title assigning device includes: a title candidate assigning means for assigning a plurality of title candidates to an image group having a plurality of images; a characteristic amount calculating means for individually calculating characteristic amounts of a plurality of images included in the image group in association with each of the plurality of title candidates; and a title determining means for determining a title representing the image group from among the title candidates based upon a characteristic amount of each of the images calculated for each of the title candidates.

Abstract: Systems and methods for segmenting video. A segmentation application executing on a computing device receives a video including video frames. The segmentation application calculates, using a predictive model trained to evaluate quality of video frames, a first aesthetic score for a first video frame and a second aesthetic score for a second video frame. The segmentation application determines that the first aesthetic score and the second aesthetic score differ by a quality threshold and that a number of frames between the first video frame and the second video frame exceeds a duration threshold. The segmentation application creates a video segment by merging a subset of video frames ranging from the first video frame to an segment-end frame preceding the second video frame.

Abstract: A method for generating an environmental profile is provided. The method for generating environmental profile includes generating an image of an environment by capturing the environment with at least one recording device, detecting a change of an object in the environment based on the image, and generating an environmental profile based on the change of the object.

Abstract: An electronic system includes a circuitry configured to obtain a sequence of frames of an object under different viewing angles at consecutive time instances. For a first time instance, the circuitry generates a point cloud descriptive for an external surface of the object on basis of (i) a point cloud obtained for a second time instance preceding the first time instance and (ii) disparity information concerning a frame captured at the first time instance.

Abstract: Disclosed is a 3D scanner comprising at least one scanning module for acquiring three-dimensional coordinates of a surface of an object and a positioning device on which the object is placeable or fixable, whereby the positioning device is movable relative to the at least one scanning module, and the 3D scanner further comprising at least one accelerometer, which is configured to measure a change of the position of the positioning device relative to the scanning module, and a first processing unit which is connected to the at least one scanning module and to the positioning device for receiving data. Also disclosed is a method for scanning a surface of an object to acquire three dimensional (3D) coordinates of the surface.

Abstract: Systems, methods, and non-transitory computer-readable media can generate a saliency prediction model for identifying salient points of interest that appear during presentation of content items, provide at least one frame of a content item to the saliency prediction model, and obtain information describing at least a first salient point of interest that appears in the at least one frame from the saliency prediction model, wherein the first salient point of interest is predicted to be of interest to one or more users accessing the content item.

Abstract: A method for processing content in an electronic device and an electronic device for doing the same are provided. The method includes acquiring content including at least one character, and performing at least one of classifying the acquired content into at least one of a plurality of categories by analyzing the acquired content or generating vector images including a vector image corresponding to the at least one character based on the acquired content and displaying at least a part of the vector images on a display functionally connected to the electronic device.

Abstract: In an image analyzing apparatus, a controller in a first analyzing process performs: sequentially identifying line pixel groups from a first side in a first direction; and determining whether a first-type pixel not contiguous to a first subject group constituted by at least one first-type pixel contiguous to each other in a second direction is present in a first region surrounding the first subject group, using first relevant information relating to each line pixel group located on the first side. In a second analyzing process, the controller performs: sequentially identifying the line pixel groups from a second side in the first direction; and determining whether the first-type pixel not contiguous to the first subject group is present in a second region surrounding the first subject group, using second relevant information relating to each line pixel group located on a second side.