Some embodiments of the present disclosure relate to systems and methods including generating, by infrared spectroscopy, spectra data identifying quantities and associated wavelengths of radiation absorption for each of a plurality of wine samples as determined by the infrared spectroscopy; converting the spectra data for each wine sample to a set of discretized data; transforming the discretized data into a visual image representation of each respective wine sample, the visual image representation of each wine sample being an optically recognizable representation of the corresponding converted set of discretized data; and storing a record including the visual image representation of each wine sample in a memory.

A computer system can automatically analyze a video of a physical activity and provide corresponding feedback. For example, the system can receive a video file including image frames showing an entity performing a physical activity that involves a sequence of movement phases. The system can generate coordinate sets by performing image analysis on the image frames. The system can provide the coordinate sets as input to a trained model, the trained model being configured to assign scores and movement phases to the image frames based on the coordinate sets. The system can then select a particular movement phase for which to provide feedback, based on the scores and movement phases assigned to the image frames. The system can generate the feedback for the entity about their performance of the particular movement phase, which may improve the entity's future performance of that particular movement phase.

An electronic device includes memory circuitry, and processor circuitry having an action detection circuitry configured to operate according to an action detection model for detecting an action based on a machine-learning scheme. The processor circuitry being configured to obtain sensor data; generate, based on the sensor data, a set of features associated with a frame; determine, based on the set, using the action detection model, whether the frame corresponds to a sub-action; apply a nondeterministic finite automaton, NFA, scheme, to the determined sub-action for the frame, wherein the NFA scheme has a set of states associated with corresponding sub-actions and is configured to output one or more action classes; determine, using the NFA scheme, an action class; detect the action based on the action class; and increment an action counter based on the detected action.

An object of the disclosure is to provide flexible and highly accurate activity detection means. Provided is an activity detection device including: an input unit that inputs an image sequence including a first image and a second image; an object detection unit that detects a first object in the image sequence; a component model unit that generates first characteristic information characterizing the first object and includes at least one individually trainable component model; and an activity detection unit that generates a first object state corresponding to the first object in the first image and a second object state corresponding to the first object in the second image based on the first characteristic information and determines an activity related to the first object based on the first and second object states.

An image classifying device is provided in the invention. The image classifying device includes a storage device, a calculation circuit and a classifying circuit. The storage device stores information corresponding to a plurality of image classes. The calculation circuit obtains a target image from an image extracting device and obtains the feature vector of the target image. The calculation circuit obtains a first estimation result corresponding to the target image based on the information corresponding to the plurality of image classes and the feature vector and obtains a second estimation result corresponding to the target image based on a reference image, wherein the reference image corresponds to one of the image classes. The classifying circuit adds the target image into one of the image classes based on the first estimation result and the second estimation result.

A hidden semi-Markov model includes plural second hidden Markov models each containing plural first hidden Markov models using types of movement of a person as states. The plural second hidden Markov models each use partial actions that are parts of actions determined by combining plural movements as states. In the hidden semi-Markov model observation probabilities are leant for each type of the movements of the plural first hidden Markov models using unsupervised learning. The learnt observation probabilities are fixed, and input first supervised data is augmented to give second supervised data, and transition probabilities of the movements of the first hidden Markov models are learned by supervised learning in which the second supervised data is employed. The learnt observation probabilities and transition probabilities are employed to build the hidden semi-Markov model that is a model for estimating segments of the partial actions.

A method includes receiving a directive from a user to find an object in a geographical area, wherein the object is identified with an input label selected from a set of labels, obtaining sensor data in response to the directive for a real world physical object in the geographical area using one or more sensors, processing the sensor data with a plurality of automatic target recognition (ATR) algorithms to assign a respective ATR label from the set of labels and a respective confidence level to the real world physical object, and receiving modeled relationships within the set of labels using a probabilistic model based on a priori knowledge encoded in a set of model parameters. The method includes inferring an updated confidence level that the real world physical object actually corresponds to the input label based on the ATR labels and confidences and based on the probabilistic model.

A method for identifying or for verifying the identity of a user, using a plurality, of previously acquired reference signature vectors, a handwritten signature of the user and at least one additional item of handwritten information linked to the user that arc affixed beforehand to an in particular mobile digital sensor, in which method: a) said handwritten signature of the user and said at least one additional item of information are fused in order to generate at least one test signature vector, b) said at least one test signature vector is compared with a plurality of said reference signature vectors, and c) a likelihood score is generated on the basis at least of this comparison in order to identify or to verify the identity of the user.

A method includes receiving a directive from a user to find an object in a geographical area, wherein the object is identified with an input label selected from a set of labels, obtaining sensor data in response to the directive for a real world physical object in the geographical area using one or more sensors, processing the sensor data with a plurality of automatic target recognition (ATR) algorithms to assign a respective ATR label from the set of labels and a respective confidence level to the real world physical object, and receiving modeled relationships within the set of labels using a probabilistic model based on a priori knowledge encoded in a set of model parameters. The method includes inferring an updated confidence level that the real world physical object actually corresponds to the input label based on the ATR labels and confidences and based on the probabilistic model.

Methods, devices, and systems for false target detection for airport traffic control are described herein. One device includes a user interface, a memory, and a processor configured to execute executable instructions stored in the memory to receive one or more sensor reports from one or more sensors, aggregate data that corresponds to a particular target from the one or more sensor reports, determine the particular target is a false target responsive to only one of the sensor reports including data that corresponds to the particular target, and display the particular target as a false target on the user interface responsive to determining the particular target is a false target.