Abstract: Aspects of the subject matter disclosed herein include methods, systems, and other techniques for training, in a first phase, an object classifier neural network with a first set of training data, the first set of training data including a first plurality of training examples, each training example in the first set of training data being labeled with a coarse-object classification; and training, in a second phase after completion of the first phase, the object classifier neural network with a second set of training data, the second set of training data including a second plurality of training examples, each training example in the second set of training data being labeled with a fine-object classification.

Abstract: A defect inspecting apparatus includes a reference image generator configured to generate a first reference image and a second reference image from design layout data. An image inspector is configured to obtain a first inspection image of a first inspection region of a photomask and a second inspection image of a second inspection region of the photomask. An operation processor is configured to extract a first coordinate offset by comparing the first inspection image with the first reference image and to extract a second coordinate offset by comparing the second inspection image with the second reference image.

Abstract: A method for detecting images of fraudulently generated or photocopied secure credential documents using texture analysis includes receiving, by one or more processors, an image of a secure credential document from a computer device. The one or more processors segment the image of the secure credential document into multiple regions. For each region of the multiple regions, the one or more processors extract local high-resolution texture features from the image of the secure credential document. The one or more processors generate a score based on the local high-resolution texture features using a machine learning model. The score is indicative of a likelihood that the image of the secure credential document is fraudulently generated or photocopied. The one or more processors transmit a message to a display device indicating that the image of the secure credential document is fraudulently generated or photocopied.

Abstract: The disclosure is inputting a first image captured an image of an authentication target; inputting a second image captured an image of a right eye or a left eye of the target; determining whether the second image is of a left eye or a right eye of the target based on information including the first image, and outputting a determination result as left/right information in association with the second image; detecting an overlap between a region including the second image and a predetermined region in the first image; calculating a verification score by comparing characteristic information that are related to the left/right information with iris characteristic information calculated from the second image, and calculating a first weighted verification score obtained by weighting the verification score with a detection result; and authenticating a target in the second image based on the first weighted verification score, and outputting an authentication result.

Abstract: The embodiments of the present disclosure disclose a driving state analysis method. The driving state analysis method includes: performing fatigue state detection and distraction state detection for a driver on a driver image to obtain a fatigue state detection result and a distraction state detection result; in response to one of the fatigue state detection result and the distraction state detection result satisfying a predetermined alarm condition, outputting alarm information of a corresponding detection result that satisfies the predetermined alarm condition; and/or, in response to both the fatigue state detection result and the distraction state detection result satisfying the predetermined alarm condition, outputting alarm information of the fatigue state detection result that satisfies the predetermined alarm condition.

Abstract: Systems, methods, and non-transitory computer readable media are provided for labeling depictions of objects within images. An image may be obtained. The image may include a depiction of an object. A user's marking of a set of dots within the image may be received. The set of dots may include one or more dots. The set of dots may be positioned within or near the depiction of the object. The depiction of the object within the image may be labeled based on the set of dots.

Abstract: A system and method for contactless blood pressure determination. The method includes: receiving a captured image sequence; determining, using a trained hemoglobin concentration (HC) changes machine learning model, bit values from a set of bitplanes in the captured image sequence that represent the HC changes of the subject; determining a blood flow data signal; extracting one or more domain knowledge signals associated with the determination of blood pressure; building a trained blood pressure machine learning model with a blood pressure training set, the blood pressure training set including the blood flow data signal of the one or more predetermined ROIs and the one or more domain knowledge signals; determining, using the blood pressure machine learning model trained with a blood pressure training set, an estimation of blood pressure; and outputting the determination of blood pressure.

Abstract: A system for image segmentation is provided. The system may obtain a target image including an ROI, and segment a preliminary region representative of the ROI from the target image using a first ROI segmentation model corresponding to a first image resolution. The system may segment a target region representative of the ROI from the preliminary region using a second ROI segmentation model corresponding to a second image resolution. At least one model of the first and second ROI segmentation models may at least include a first convolutional layer and a second convolutional layer downstream to the first convolutional layer. A count of input channels of the first convolutional layer may be greater than a count of output channels of the first convolutional layer, and a count of input channels of the second convolutional layer may be smaller than a count of output channels of the second convolutional layer.

Abstract: An adaptive spoofing method includes obtaining multiple images of a spoof representation of a human user. The multiple images are captured automatically by an image capture device, and each of the multiple images is captured at a different relative position between the image capture device and the spoof representation. The method also includes: executing a biometric authentication process separately on each of at least a subset of the multiple images; determining that the biometric authentication process authenticates the human user based on at least a first image from the subset; and identifying a relative position between the image capture device and the spoof representation corresponding to the first image as a failure condition associated with the biometric authentication process.

Abstract: An exemplary system, method and computer-accessible medium for determining an effect(s) of a convulsive stimulation therapy(ies) on a patient(s) can be provided, which can include, for example, receiving first information related to a visual network or a default mode network of a brain of the patient(s), receiving second information related to a subgenual ACC or a default mode (DMN network of the brain of the patient(s), and determining the effect(s) of the convulsive stimulation therapy(ies) based on a relationship between the first information and the second information. The convulsive stimulation therapy(ies) can be an electroconvulsive therapy or a magnetic seizure therapy.

Abstract: An electronic device includes an eye gaze detection unit that detects a gaze position of a user, and a control unit that changes a luminance value of a background image displayed on a display unit in at least two steps. The control unit performs control to, when the luminance value is a first luminance value, change the luminance value from the first luminance value to a second luminance value after the eye gaze detection unit detects a plurality of gaze positions of the user including at least the gaze position of the user in a case where an item is displayed at a first position on the display unit and the gaze position of the user in a case where the item is displayed at a second position thereon. The eye gaze detection unit detects the gaze position of the user when the luminance value is the second luminance value.

Abstract: According to various, but not necessarily all, embodiments there is provided an apparatus comprising means for: capturing at least one image of a cornea of the user; processing the at least one image to detect one or more objects and/or activities based on one or more reflections by a peripheral portion of the cornea; and providing an alert to the user for alerting the user to the detection of the one or more objects and/or activities.

Abstract: An image processing device including: a first feature quantity selecting unit configured to select a first feature quantity of a document image that is a character recognition target among first feature quantities that are recoded in advance and represent features of character strings of an item; a character recognition processing unit configured to perform a character recognition process for the document image; a character string selecting unit configured to select a character string of a specific item corresponding to the first feature quantity among the character strings acquired as a result of the character recognition process; and a determination result acquiring unit configured to acquire a determination result indicating whether or not a character string that has been input in advance matches the character string of the specific item in a case in which the character string selecting unit has not selected any one of the character strings.

Abstract: In embodiments, obtaining a plurality of image sets associated with a geographical region and a time period, wherein each image set of the plurality of image sets comprises multi-spectral and time series images that depict a respective particular portion of the geographical region during the time period, and predicting one or more crop types growing in each of particular locations within the particular portion of the geographical region associated with an image set of the plurality of image sets. Determining a crop type classification for each of the particular locations based on the predicted one or more crop types for the respective particular locations, and generating a crop indicative image comprising at least one image of the multi-spectral and time series images of the image set overlaid with indications of the crop type classification determined for the respective particular locations.

Abstract: A conformance testing method including: obtaining a testing symbol pattern in an optical signal; performing equalization compensation on the testing symbol pattern; generating a testing eye pattern; calculating a value of a first parameter based on the testing eye pattern and a noise enhancement coefficient, where the first parameter is used to determine a transmitter dispersion eye pattern closure degree of the optical transmitter; and when the value of the first parameter is less than or equal to a preset threshold, determining that conformance testing on the optical signal succeeds.

Abstract: An exemplary system, method, and computer-accessible medium for determining a position or a characteristic of a target(s) for a transcranial magnetic stimulation (TMS) treatment of a patient(s) can be provided, which can include, for example, receiving imaging information of a portion(s) of a head of the patient(s), and determining the position or the characteristic of the target(s) for the TMS treatment of the patient(s) based on the imaging information. The imaging information can be magnetic resonance imaging information. The imaging information can include information regarding a brain and a skull of the patient(s). The position or the characteristic of the target(s) can be determined by identifying (i) the skull, and (ii) a parcel in a section(s) of a brain of the patient(s). The parcel can a dorsolateral prefrontal cortex (DLPFC) parcel. The DLPFC parcel can be identified using a parcellation procedure, which can be a human connectome pipeline procedure.

Abstract: Systems, devices, media and methods are presented for a human pose tracking framework. The human pose tracking framework may identify a message with video frames, generate, using a composite convolutional neural network, joint data representing joint locations of a human depicted in the video frames, the generating of the joint data by the composite convolutional neural network done by a deep convolutional neural network operating on one portion of the video frames, a shallow convolutional neural network operating on a another portion of the video frames, and tracking the joint locations using a one-shot learner neural network that is trained to track the joint locations based on a concatenation of feature maps and a convolutional pose machine. The human pose tracking framework may store, the joint locations, and cause presentation of a rendition of the joint locations on a user interface of a client device.

Abstract: Inputs from sensors (e.g., image and environmental sensors) are used for real-time optimization of plant growth in indoor farms by adjusting the light provided to the plants and other environmental factors. The sensors use wireless connectivity to create an Internet of Things network. The optimization is determined using machine-learning analysis and image recognition of the plants being grown. Once a machine-learning model has been generated and/or trained in the cloud, the model is deployed to an edge device located at the indoor farm to overcome connectivity issues between the sensors and the cloud. Plants in an indoor farm are continuously monitored and the light energy intensity and spectral output are automatically adjusted to optimal levels at optimal times to create better crops. The methods and systems are self-regulating in that light controls the plant's growth, and the plant's growth in-turn controls the spectral output and intensity of the light.

Abstract: Apparatuses and methods associated with personal history in a track and trace system are disclosed herein. In embodiments, a method includes acquiring first information corresponding to a physical object; identifying first authentication data based on the first information; storing the first authentication data in a database system; subsequent to storing the first authentication data, acquiring second information corresponding to a target physical object and identifying second authentication data based on the second information; identifying third information corresponding to the target physical object, querying the database system to seek a matching record based on the second authentication data; in the case that a matching record is returned responsive to the querying, updating the matching record with an indication that the second authentication data was matched to it and updating history data or said matching record based on said third information. Other embodiments may be disclosed or claimed.

Abstract: A method for correcting an image includes: orthographically correcting an image; removing a curtain artifact by applying a first filter to the orthographically corrected image; correcting brightness of the image, from which the curtain artifact is removed, by applying a second filter to the image, from which the curtain artifact is removed. The first filter includes a first function and a second function for a first domain and a second domain, which are orthogonal to each other in a frequency region, and the first filter is differentiable and continuous in the first domain and the second domain.