Abstract: Detecting abnormalities in vital signs of subjects of videos is provided. Aspects of the present disclosure include methods, apparatuses, and systems to detect and measure vital sign information of one or more human subjects of a video and detect abnormalities in the vital sign information. In some examples, such abnormalities can be used to indicate video data is likely altered or fraudulent. In this regard, imaging photophlethysmography (IPPG) and advanced signal processing techniques, including adaptive color beamforming, can be used to extract the vital signs of the video subjects.

Abstract: A liveliness prediction output is provided from a liveliness biometric analysis task that is determined by a deep neural network based on an image provided from an image sensor wherein the liveliness biometric analysis task is performed in a deep neural network that includes a common feature extraction neural network and a plurality of task-specific neural networks including a face detection neural network, a body pose neural network and a liveliness neural network including a region of interest (ROI) detection neural network and a texture analysis neural network to determine the liveliness biometric analysis task by inputting the image to the common feature extraction neural network to determine latent variables. The latent variables can be input to the face detection neural network and the liveliness neural network. Output from the face detection neural network can be input to the ROI detection neural network.

Abstract: The present application relates to a method for the cytometric analysis of multiple cell samples by a microscope for examining multiple cell samples under a microscope, wherein the microscope can be or is operated, selectively and/or alternatingly, in a transmission mode and/or in a fluorescence mode, and wherein at least one cell sample has at least one fluorescence marker. The method includes; moving the cell samples continuously in one plane relative to an optical system of the microscope having at least one microscope camera, wherein, during the movement of the cell samples, at least one or more images of a sub-region of the cell samples are recorded in the transmission mode or in the fluorescence mode and at least one or more images of the same sub-region of the cell samples are recorded in the fluorescence mode by at least one microscope camera.

Abstract: This document relates to hybrid eye center localization using machine learning, namely cascaded regression and hand-crafted model fitting to improve a computer. There are proposed systems and methods of eye center (iris) detection using a cascade regressor (cascade of regression forests) as well as systems and methods for training a cascaded regressor. For detection, the eyes are detected using a facial feature alignment method. The robustness of localization is improved by using both advanced features and powerful regression machinery. Localization is made more accurate by adding a robust circle fitting post-processing step. Finally, using a simple hand-crafted method for eye center localization, there is provided a method to train the cascaded regressor without the need for manually annotated training data. Evaluation of the approach shows that it achieves state-of-the-art performance.

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: According to one embodiment, an apparatus includes a first acquisition unit, a second acquisition unit, an identification unit, and an output unit. The first acquisition unit acquires a query image and a query text relating to a target object. The second acquisition unit acquires candidate images of the target object. The identification unit identifies from the candidate images a positive image containing a region demonstrating a similarity to the query image higher than or equal to a first threshold value, and identifies a position of the region in the positive image. The output unit outputs training data including the positive image, information representing the position of the region, and a correct label.

Abstract: A machine learning system can generate an image mask (e.g., a pixel mask) comprising pixel assignments for pixels. The pixels can he assigned to classes, including, for example, face, clothes, body skin, or hair. The machine learning system can be implemented. using a convolutional neural network that is configured to execute efficiently on computing devices having limited resources, such as mobile phones. The pixel mask can be used to more accurately display video effects interacting with a user or subject depicted in the image.

Abstract: An image processing apparatus, which receives an input image and detects an image of a target object based on a detection algorithm, includes a machine learning device which performs learning by using a plurality of partial images cut out from at least one input image, based on a result of detection of the image of the target object, and calculates a likelihood of the image of the target object.

Abstract: A mobile client device is localized based on a captured image by identifying where the client device is located from a set of known locations. The set of known locations is associated with a set of regions, where each region is associated with a set of key frames representing the important features of the region. Latent vectors and keypoints are calculated for each of the key frames and an image captured by the client device. The system compares the latent vectors of the captured image to the latent vectors associated with the regions to determine a subset of similar regions. The system compares the keypoints of the captured image to the keypoints associated with the regions in the subset to determine a best match. This determined location is considered the region of the client device and may be used with other localization information to maintain localization of the client device.

Abstract: An apparatus, method, system and computer-readable medium are provided for generating one or more descriptors that may potentially be associated with content, such as video or a segment of video. In some embodiments, a teaser for the content may be identified based on contextual similarity between words and/or phrases in the segment and one or more other segments, such as a previous segment. In some embodiments, an optical character recognition (OCR) technique may be applied to the content, such as banners or graphics associated with the content in order to generate or identify OCR'd text or characters. The text/characters may serve as a candidate descriptor(s). In some embodiments, one or more strings of characters or words may be compared with (pre-assigned) tags associated with the content, and if it is determined that the one or more strings or words match the tags within a threshold, the one or more strings or words may serve as a candidate descriptor(s).

Abstract: The present disclosure provides methods and systems for performing licence plate matching. A reference list comprising licence plate identifiers is obtained. At least one transformation rule is applied to the licence plate identifiers of the reference list to generate entries forming at least one augmented reference list, and storing the at least one augmented reference list in at least one database. A search request comprising an input licence plate identifier is obtained. The at least one transformation rule is applied to the input licence plate identifier to generate a search list comprising at least one search term. The at least one database is queried with the search list to identify a match between at least one of the entries of the at least one augmented reference list and the at least one search term of the search list. A signal responsive is output to identifying the match.

Abstract: Implementations of the present disclosure include receiving a training image, providing a hash pattern that is representative of the training image, applying a plurality of filters to the training image to provide a respective plurality of filtered training images, identifying a filter to be associated with the hash pattern based on the plurality of filtered training images, and storing a mapping of the filter to the hash pattern within a set of mapping in a data store.

Abstract: A system and method that identify and classify unknown microorganisms and/or known microorganisms with anomalies are provided. The system and method comprise processing images of microorganisms from an aquatic environment; extracting features from the processed images; an unsupervised partitioning algorithm for identifying and classifying known microorganisms in the aquatic environment based upon the extracted features; and a supervised classifier neural network that is trained with the unsupervised partitioning algorithm and identifies and classifies unknown microorganisms and/or known microorganisms with anomalies.

Abstract: Disclosed is an inspection device including: a machine learning device that performs machine learning on the basis of state data acquired from an inspection target and label data indicating an inspection result related to the inspection target to generate a learning model; a learning model evaluation index calculation unit that calculates a learning model evaluation index related to the learning model generated by the machine learning device as an evaluation index used to evaluate the learning model; an inspection index acquisition unit that acquires an inspection index used in an inspection; and an index value determination unit that determines whether the learning model generated by the machine learning device satisfies the inspection index on the basis of the learning model evaluation index and the inspection index and outputs a result of the determination.

Abstract: Keyword localization digital image search techniques are described. These techniques support an ability to indicate “where” a corresponding keyword is to be expressed with respect to a layout in a respective digital image resulting from a search query. The search query may also include an indication of a size of the keyword as expressed in the digital image, a number of instances of the keyword, and so forth. Additionally, the techniques and systems as described herein support real time search through use of keyword signatures.

Abstract: A device for providing reference information in laser speckle medical imaging of tissue of a subject can include a support that includes a marker for image recognition and a speckle target region that is nontransparent to light in a first illumination wavelength range and that generates a speckle pattern when illuminated with light in the first wavelength range. The support can be configured to be attachable to tissue of the subject.

Abstract: Embodiments relate to a method for determining a search region including acquiring object information of a target object included in an image query, generating a set of non-image features of the target object based on the object information, setting a search candidate region based on a user input, acquiring information associated with the search candidate region from a region database, and determining a search region based on at least one of the information associated with the search candidate region or at least part of the set of non-image features, and a system for performing the same.

Abstract: A computer-implemented method of detecting logos in a graphical rendering may comprise detecting, using a first and a second trained object detector, logos in the graphical rendering and outputting a first and a second list of detections and filtering, using at least a first and a second prior performance-based filter, the received first and second lists of detections into a first group of kept detections, a second group of discarded detections and a third group of detections. Detections in the third group of detections may be clustered in at least one cluster comprising detections that are of a same class and that are generally co-located within the electronic image. A cluster score may then be assigned to each cluster. A set of detections of logos in the graphical rendering may then be output, the set comprising the detections in the first group and a detection from each of the clusters whose assigned cluster score is greater than a respective threshold.

Abstract: Embodiments described herein may provide devices, systems, methods, and/or computer readable medium for adverse event detection and severity estimation in surgical videos. The system can train multiple models for adverse detection and severity estimation. The system can load selected models for real-time adverse event detection and severity estimation.

Abstract: A method for making texture symbols of a land use classification map is disclosed in the disclosure, including: capture of texture materials; extraction of main colors; color clustering; extraction of a texture skeleton; tile effect removal; and establishment of a texture library. The disclosure has the following advantages: definitions and classes of texture symbols are provided, and a procedure of making texture symbols is made clear; used natural texture symbols and symbolic texture symbols have clear semantic meanings, facilitating information transfer of the map; the quality and layering of the map are improved; the texture symbols can be directly used for production, to provide a fundamental support for survey and mapping of land use, and also to provide a solution for large-scale result mapping of natural resource survey. The expression of thematic maps of current land use classification is improved.