Abstract: An apparatus for training a classification model includes: a feature extraction unit configured to set, with respect to each training set of a first predetermined number of training sets, feature extraction layers, and extract features of a sample image, where at least two of the training sets at least partially overlap; a feature fusion unit configured to set, with respect to training set, feature fusion layers, and perform a fusion on the extracted features of the sample image; and a loss determination unit configured to set, with respect to training set, a loss determination layer, calculate a loss function of the sample image based on the fused feature of the sample image, and train a classification model based on the loss function. The first predetermined number of training sets share at least one layer of feature fusion layers and feature extraction layers set with respect to each training set.

Abstract: A fingerprint input method, apparatus, system and an electronic device are provided. The method includes: obtaining a fingerprint as a current fingerprint (S101); comparing the current fingerprint with a fingerprint stored in a fingerprint database (S102); storing, when it is determined that there is no fingerprint identical with the current fingerprint in the fingerprint database, the current fingerprint in the fingerprint database. It can be seen that before storing the obtained current fingerprint into the fingerprint database, it is firstly determined whether there is a fingerprint identical with the current fingerprint in the fingerprint database, and when it is determined that there is no fingerprint identical with the current fingerprint in the fingerprint database, the current fingerprint is stored in the fingerprint database, to avoid the occurrence of duplicate fingerprints in the fingerprint database, avoid the input of the duplicate fingerprints, and improve the user experience.

Abstract: Methods, apparatus, and processor-readable storage media for linking individual biometric data to protected resources accessed via user devices are provided herein. An example computer-implemented method includes obtaining biometric data associated with users of a user device and identifying information pertaining to the biometric data; outputting the identifying information pertaining to the of biometric data and identifying information pertaining to protected resources; linking at least a portion of the multiple sets of biometric data to one of the protected resources based on input from at least a first of the users in response to the outputting step; processing, in response to an authentication request in connection with an attempt to access the protected resource by the at least first user, biometric data input to the user device; and resolving the authentication request in response to a determination that the input biometric data match the biometric data linked to the protected resource.

Abstract: Described is a system and method that provides a data protection risk assessment for the overall functioning of a backup and recovery system. Accordingly, the system may provide a single overall risk assessment score that provide an operator with an “at-a-glance” overview of the entire system. Moreover, the system may account for changes that occur over time based on leveraging statistical methods to automatically generate assessment scores for various components (e.g. application, server, network, load, etc.). In order to determine a risk assessment score, the system may utilize a predictive model based on historical data. Accordingly, residual values for newly observed data may be determined using the predictive model and the system may identify potentially anomalous or high risk indicators.

Abstract: A method for identifying a user using an image of an object of the user that has a biometric characteristic of the user, like a fingerprint or a set of fingerprints of fingertips, the method comprising: obtaining, by an optical sensor of a mobile device, the image of the object; providing the image to a neural network; processing the image by the neural network, thereby identifying both, the position of the object and the object in the image; extracting, from the identified object, the biometric characteristic; storing the biometric characteristic in a storage device and/or providing at least the biometric characteristic as input to an identification means, comprising processing the input in order to determine whether the biometric characteristic identifies the user.

Abstract: An image processing method includes: receiving specific data of an image component of input image for a specific color encoding channel wherein the specific data of the image component is formed by the image component's multiple raw values having identical image characteristics; spatially classifying pixel units within the input image into a plurality of blocks according to a specific block size; for each block, obtaining values of the image component of pixel units within each block to calculate the average value of the image component of the each block; generating interpolated values of the image component of an interpolated image according to values of the image component corresponding to the plurality of blocks; and, blending the interpolated values of the image component of the interpolated image with original/raw values of the image component of the input image to generate an output image having modified values of the image component.

Abstract: A sensor device includes an array sensor having a plurality of detection elements arrayed in one or two dimensional manner, a signal processing unit configured to acquire a detection signal by the array sensor and perform signal processing, and a calculation unit. The calculation unit detects an object from the detection signal by the array sensor, and gives an instruction, to the signal processing unit, on region information generated on the basis of the detection of the object as region information regarding the acquisition of the detection signal from the array sensor or the signal processing for the detection signal.

Abstract: A symbol reading device includes a plurality of reading units and a processor. The reading units read symbols on articles conveyed along a conveyance path. The processor is configured to acquire symbol information from a plurality of articles and store the symbol information in association with each of the reading units that read the symbols on respective articles, then identify a reading unit that can be substituted by another reading unit based on the symbol information associated with the reading units. The identified reading unit in then placed into a power saving state.

Abstract: Systems and methods are provided for performing temporally consistent depth map generation by implementing acts of obtaining a first stereo pair of images of a scene associated with a first timepoint and a first pose, generating a first depth map of the scene based on the first stereo pair of images, obtaining a second stereo pair of images of the scene associated with at a second timepoint and a second pose, generating a reprojected first depth map by reprojecting the first depth map to align the first depth map with the second stereo pair of images, and generating a second depth map that corresponds to the second stereo pair of images using the reprojected first depth map.

Abstract: Systems and methods are provided performing for low compute depth map generation by implementing acts of obtaining a stereo pair of images of a scene, downsampling the stereo pair of images, generating a depth map by stereo matching the downsampled stereo pair of images, and generating an upsampled depth map based on the depth map using an edge-preserving filter for obtaining at least some data of at least one image of the stereo pair of images.

Abstract: A method for biometrics spoofing detection according to an embodiment of the present disclosure includes receiving a biometric authentication request from an application, acquiring biometrics at a sensor, and applying a machine learning-based anti-spoofing scheme to the biometrics based on an authentication purpose of the biometrics. The anti-spoofing scheme for biometrics of the present disclosure may include a deep neural network generated by machine learning, and may be used in an Internet of Things environment using a 5G network.

Abstract: A computer-implemented method for providing a cardiac motion classification based on Cardiac Magnetic Resonance (CMR) image data, wherein the CMR image data comprise a plurality of image frames, I(x, y, z, t), acquired for respective two-dimensional slices in at least one longitudinal direction, z, of the heart and for a plurality of times, t, the method including: a myocardium segmentation step of inputting the plurality of image frames into two or more trained neural networks, applying the trained neural networks in parallel, and fusing an output of each of the trained neural networks into a single output indicating a segmentation, for each of the plurality of image frames, between a first portion indicating muscle tissue of the heart and a second portion indicating surrounding tissue of the heart muscle, and determining a corresponding mask of muscle tissue for the first portion; a slice classification step of assigning each of the plurality of image frames in each slice, z, to an anatomic layer of the hea

Abstract: According to one embodiment, a reading support system includes a processing device. The processing device includes an extractor and a type determiner. The extractor extracts a plurality of regions from a candidate region. The candidate region is a candidate of a region in which a meter is imaged. The regions respectively include a plurality of characters of the meter. The type determiner determines a type of the meter based on positions of the regions.

Abstract: The present disclosure relates to method and system for performing classification of real-time input sample using compressed classification model. Classification system receives classification model configured to classify training input sample. Relevant neurons are identified from neurons of the classification model. Classification error is identified for each class. Reward value is determined for the relevant neurons based on relevance score of each neuron and the classification error. Optimal image is generated for each class based on the reward value of the relevant neurons. The optimal image is provided to the classification model for generating classification error vector for each class. The classification error vector is used for identifying pure neurons from the relevant neurons. A compressed classification model comprising the pure neurons is generated. The generated compressed classification model is used for performing the classification of real-time input sample.

Abstract: A region clipping method for clipping a region of a target object out of an image is performed by a computer. The method includes: referencing a memory storing three-dimensional positional information of the target object; and calculating, by using information on an imaging position for the image and orientation for the image, the region of the target object within the image from the three-dimensional positional information.

Abstract: A method of detecting biometric feature with an electronic device and the method includes the following steps. Firstly, a detection region is provided on the electronic, the electronic device includes a plurality of first sensor units and a plurality of second sensor units. Then, a first scanning light is generated in the detection region, and the first scanning light is sensed by the plurality of first sensor units. Next, a second scanning light is generated in the detection region, and the second scanning light is sensed by the plurality of second sensor units. Finally, a biometric feature is determined.

Abstract: A system for indexing animated content receives detections extracted from a media file, where each one of the detections includes an image extracted from a corresponding frame of the media file that corresponds to a detected instance of an animated character. The system determines, for each of the received detections, an embedding defining a set of characteristics for the detected instance. The embedding associated with each detection is provided to a grouping engine that is configured to dynamically configure at least one grouping parameter based on a total number of the detections received. The grouping engine is also configured to sort the detections into groups using the grouping parameter and the embedding for each detection. A character ID is assigned to each one of the groups of detections, and the system indexes the groups of detections in a database in association with the character ID assigned to each group.

Abstract: A convolutional neural network (CNN) is applied to identifying tumors in a histological image. The CNN has one channel assigned to each of a plurality of tissue classes that are to be identified, there being at least one class for each of non-tumorous and tumorous tissue types. Multi-stage convolution is performed on image patches extracted from the histological image followed by multi-stage transpose convolution to recover a layer matched in size to the input image patch. The output image patch thus has a one-to-one pixel-to-pixel correspondence with the input image patch such that each pixel in the output image patch has assigned to it one of the multiple available classes. The output image patches are then assembled into a probability map that can be co-rendered with the histological image either alongside it or over it as an overlay. The probability map can then be stored linked to the histological image.

Abstract: An approach is provided for a redundant feature detection engine. The approach, for instance, involves segmenting an input image into a plurality of grid cells for processing by the redundant feature detection engine. The redundant feature detection engine includes a neural network. The approach also involves, for each of the plurality of grid cells, initiating a prediction of an object code by the redundant feature detection engine. The object code is a predicted feature that uniquely identifies an object depicted in the input image. The approach further involves aggregating the plurality of grid cells into one or more clusters based on the object code predicted for said each grid cell. The approach further involves predicting one or more features of the object corresponding to a respective cluster of the one or more clusters by merging one or more feature prediction outputs of said each grid cell in the respective cluster.

Abstract: A system and method for improving a video characteristic of a video stream is described. According to various implementations of the invention, a changed region between a later-in-time image frame and an earlier-in-time image frame and an unchanged region between such two image frames are determined. A new improvement to the video characteristic is determined and applied to the changed region of the later-in-time image frame. A prior improvement to the video characteristic that was determined for the earlier-in-time image frame is applied to the unchanged region of the later-in-time image frame.