Abstract: A system and method for training a neural network to denoise images. One noise realization is paired to an ensemble of training-ready noise realizations, and fed into a neural network for training. Training datasets can also be retrospectively generated based on existing patient studies to increase the number of training datasets.

Abstract: A method of tracking an object across a stream of images comprises determining a region of interest (ROI) bounding the object in an initial frame of an image stream. A HOG map is provided for the ROI by: dividing the ROI into an array of M×N cells, each cell comprising a plurality of image pixels; and determining a HOG for each of the cells. The HOG map is stored as indicative of the features of the object. Subsequent frames are acquired from the stream of images. The frames are scanned ROI by ROI to identify a candidate ROI having a HOG map best matching the stored HOG map features. If the match meets a threshold, the stored HOG map indicative of the features of the object is updated according to the HOG map for the best matching candidate ROI.

Abstract: An evaluation system is a system configured to evaluate coverage of an evaluation target by using a captured image of the evaluation target. The evaluation system includes: an image acquisition unit configured to acquire the captured image; a correction unit configured to generate an evaluation image by correcting the captured image; an evaluation unit configured to evaluate the coverage based on the evaluation image; and an output unit configured to output a result of the evaluation carried out by the evaluation unit, wherein the correction unit extracts an evaluation region from the captured image based on the size of a dent region included in the captured image and generates the evaluation image based on the evaluation region, and the dent region is an image of a dent formed on the evaluation target.

Abstract: An image restoration method and apparatus are provided. The image restoration method includes acquiring a target image, and acquiring a restoration image of the target image from an image restoration model to which the target image and pixel position information of the target image are input.

Abstract: This application discloses a method for detecting a target in a video, performed by a computing device. The method includes applying a target detection operation to a first frame in the video, to determine a first target detection result of the target in the first frame; applying a target tracking operation to a second frame after the first frame in the video, to determine changes of the target between the first frame and the second frame; and determining a second target detection result of the target in the second frame according to the first target detection result and the changes of the target between the first frame and the second frame.

Abstract: A method for motion recognition and embedding is disclosed. The method may include receiving a plurality of frames of an input video for extracting a feature vector of a motion in the plurality of frames, generating a plurality of sets of one or more motion component bits based on the feature vector and a plurality of classifiers, the plurality of sets corresponding to the plurality of classifiers, each set of one or more motion component bits representing a physical or mechanical attribute of the motion; and generating a motion code for a machine to execute the motion by combining the plurality of sets of one or more motion component bits. Other aspects, embodiments, and features are also claimed and described.

Abstract: Various methods and systems are described for obtaining at least one CTA perfusion functional map from Time Resolved Helical CTA (TRH-CTA) image data. At least one processor may be configured to preprocess the TRH-CTA helical image data to generate preprocessed TRH-CTA helical image data; generate time density curve data for a plurality of voxels from the preprocessed TRH-CTA helical image data for an axial imaging slice, where the time density curve data comprise intensity values for different phases of the preprocessed TRH-CTA helical image data arranged sequentially in time; generate at least one perfusion functional map for the axial imaging slice by at least one of: (1) applying at least one mapping function to different phases of the time density curve data corresponding to the axial imaging slice; (2) applying a deconvolution method to the time density curve data; and (3) applying a non-deconvolution method to the time density curve data; and perform spatial filtering on the perfusion functional map.

Abstract: A method for recognizing at least one object in at least one input image. In the method, a template image of the object is processed by a first convolutional neural network (CNN) to form at least one template feature map; the input image is processed by a second CNN to form at least one input feature map; the at least one template feature map is compared to the at least one input feature map; it is evaluated from the result of the comparison whether and possibly at which position the object is contained in the input image, the convolutional neural networks each containing multiple convolutional layers, and at least one of the convolutional layers being at least partially formed from at least two filters, which are convertible into one another by a scaling operation.

Abstract: Systems and methods are provided for detecting one or more anomalous events in video. Histogram-based noise cleansing, higher-order deep convolutional neural network-based feature extraction, instance segmentation, instance summation, difference calculation, and normalization can be used. Human-in-loop systems and methods can facilitate human decisions for anomaly detection. The decision of an anomaly event can be made by, for example, the instance difference value(s).

Abstract: Disclosed are methods and systems that include obtaining at least one image of a dendritic structure, analyzing the at least one image to identify one or more features associated with the dendritic structure, and determining a numerical value associated with the dendritic structure based on the one or more features.

Abstract: An asset identification and tracking system includes one or more monitoring units configured to monitor at least one designated area. Each of the monitoring units includes an imaging device and one or more processors. The imaging device is configured to generate image data depicting one or more mobile assets that move through the at least one designated area. The one or more processors are operably coupled to the imaging device and configured to analyze the image data to detect and decipher one or more identifiers that are displayed on a particular mobile asset of the one or more mobile assets that move through the at least one designated area. The one or more processors are further configured to generate a detection message that includes the one or more identifiers for communication to an asset control system.

Abstract: An electronic device and a method for controlling the electronic device are provided. The method for controlling the electronic device includes, based on an occurrence of an event for outputting information being determined, obtaining data for determining a context corresponding to the electronic device, inputting the obtained data to a first model trained by an artificial intelligence algorithm and obtaining information about a person located in a vicinity of the electronic device, inputting the obtained information about the person and information about the event to a second model trained by an artificial intelligence algorithm and obtaining output information corresponding to the event, and providing the obtained output information.

Abstract: A method for generating a monitoring image. The method includes: providing an image sequence of the surroundings to be monitored with the aid of an imaging system; determining at least one monitoring area and at least one periphery area of at least one image of the image sequence with the aid of a learning-based semantic segmentation method; compressing the monitoring area of the at least one image of the image sequence with a first compression quality; and compressing the periphery area of the at least one image of the image sequence with a second compression quality to generate the compressed monitoring image, the second compression quality being lower than the first compression quality.

Abstract: Data may be abstracted and/or masked prior to being provided to a machine learning model for training. A machine learning model may provide a confidence level associated with a result. If the confidence level is too high, the machine learning model or an application including the machine learning model may refrain from providing the result as an output. In some examples, the machine learning model may provide a “second best” result that has an acceptable confidence level. In other examples, an error signal may be provided as the output. In accordance with examples of the present disclosure, data may be abstracted and/or masked prior to being provided to a machine learning model for training and confidence levels of results of the trained machine learning model may be used to determine when a result should be withheld.

Abstract: A system and processing methods for configuring and utilizing a convolutional neural network (CNN) for plant disease recognition are disclosed. In some embodiments, the system is programmed to collect photos of infected plants or leaves where regions showing symptoms of infecting diseases are marked. Each photo may have multiple marked regions. Depending on how the symptoms are sized or clustered, one marked region may include only one lesion caused by one disease, while another may include multiple, closely-spaced lesions caused by one disease. The system is programmed to determine anchor boxes having distinct aspect ratios from these marked regions for each convolutional layer of a single shot multibox detector (SSD). For certain types of plants, common diseases lead to relatively many aspect ratios, some having relatively extreme values. The system is programmed to then train the SSD using the marked regions and the anchor boxes and apply the SSD to new photos to identify diseased plants.

Abstract: Systems and methods are provided for counting particles in a fluid flow. In an aspect, coordinates of particles are obtained from video data of particles in a fluid, the video data made up of a sequence of image frames. The particle positions are linked in each pair of consecutive image frames of the video data. The linked particle positions are used to calculate particle trajectories through sequential image frames of the video data, and the particles are counted based on the particle trajectory. In another aspect, the particle positons within each image frame are transformed to estimated positions within a common coordinate frame. The estimated particle positions of a particle are grouped into a cluster center, and the particle count is calculated based on the cluster centers.

Abstract: Systems and methods are directed to a method for estimation of an object state from image data. The method can include obtaining two-dimensional image data depicting an object. The method can include processing, with an estimation portion of a machine-learned object state estimation model, the two-dimensional image data to obtain an initial estimated state of the object. The method can include, for each of one or more refinement iterations, obtaining a previous loss value associated with a previous estimated state for the object, processing the previous loss value to obtain a current estimated state of the object, and evaluating a loss function to determine a loss value associated with the current estimated state of the object. The method can include providing a final estimated state for the object.

Abstract: Techniques for distributed object detection and tracking are described. In an example method, a first current frame from a series of frames is sent to a first node, for detection of a first object. After object detection information for the first object is received in return, a second node is selected, and a second current frame is sent to the second node for an updated object detection. In addition, while waiting for the results of the updated object detection, two or more frames following the second current frame are sent to respective tracking nodes. Object modelling information indicating location and/or classification of one or more objects, as derived from the previously received object detection information is also sent to each of the respective tracking nodes. Tracking information for the first object is received from each of the respective tracking nodes.

Abstract: A computing system determines accuracy of sport-related information extracted from a time sequence of digital video frames that represent a sport event, the extracted sport-related information including an attribute that changes over the time sequence. The computing system (a) detects, based on the extracted sport-related information, a pattern of change of the attribute over the time sequence and (b) makes a determination of whether the detected pattern is an expected pattern of change associated with the sport event. If the determination is that the detected pattern is the expected pattern, then, responsive to making the determination, the computing system takes a first action that corresponds to the sport-related information being accurate. Whereas, if the determination is that the detected pattern is not the expected pattern, then, responsive to making the determination, the computing system takes a second action that corresponds to the sport-related information being inaccurate.

Abstract: An apparatus, method and computer program is described comprising: receiving video data for a scene; determining a movement measurement for at least some of a plurality of subframes of the video data; weighting the movement measurements to generate a plurality of weighted movement measurements, wherein the weighting is dependent on the subframe; and generating a movement indication for the scene from a combination of some or all of the weighted movement measurements.