Abstract: A system for processing images captured in a retail store and automatically identifying misplaced products is provided. The system may comprise at least one processor configured to receive one or more images captured by one or more image sensors from an environment of a retail store, detect in the one or more images a first product, determine that the first product is not located in the first correct display location, cause an issuance of a user-notification associated with the first product, detect in the one or more images a second product, determine that the second product is not located in the second correct display location, and after determining that the second product is not located in the second correct display location and when the second urgency level is lower than the first urgency level, withhold issuance of a user-notification associated with the second product.

Abstract: In various examples, the present disclosure relates to using temporal filters for automated real-time classification. The technology described herein improves the performance of a multiclass classifier that may be used to classify a temporal sequence of input signals—such as input signals representative of video frames. A performance improvement may be achieved, at least in part, by applying a temporal filter to an output of the multiclass classifier. For example, the temporal filter may leverage classifications associated with preceding input signals to improve the final classification given to a subsequent signal. In some embodiments, the temporal filter may also use data from a confusion matrix to correct for the probable occurrence of certain types of classification errors. The temporal filter may be a linear filter, a nonlinear filter, an adaptive filter, and/or a statistical filter.

Abstract: The subject technology provides object recognition systems and methods that can be used to identify objects of interest in an image. An image such as live preview may be generated by a display component of the electronic device and an object of interest may be detected in the image. The detected object of interest may be classified using a classification model. Subsequent to classification, a confidence level in identifying the object of interest may be determined. In response to determining that the confidence level does not meet a confidence level threshold for identifying the object of interest, a request for a user input is generated. Based on the user input, the object of interest is identified using an object recognition model.

Abstract: A management and control system is provided for a user to interface with an inspecting apparatus having at least one digital optical instrument. The management and control system comprises a processor configured to receive images from the at least one digital optical instrument, analyze the images, and transmit instructions to the inspecting apparatus, and a display configured to display analysis of the images wherein the user is capable of interfacing and providing instructions to the inspecting apparatus based on the analysis of the images, wherein the display simultaneously displays histograms and thumbnail-image generated in the processor based on the images. A method for controlling and managing the inspecting apparatus for items as well as an UI are disclosed.

Abstract: An edge device is capable of requesting a server including a first processing processor dedicated to specific processing to execute predetermined processing, and the edge device includes: a determination unit configured to determine whether the predetermined processing is processable by a second processing processor dedicated to the specific processing included in the edge device; and a control unit configured to cause the second processing processor of the edge device to execute the predetermined processing in a case where it is determined that the predetermined processing is processable.

Abstract: Systems and methods of the present disclosure can include a computer-implemented method for efficient machine-learned model training. The method can include obtaining a plurality of training samples for a machine-learned model. The method can include, for one or more first training iterations, training, based at least in part on a first regularization magnitude configured to control a relative effect of one or more regularization techniques, the machine-learned model using one or more respective first training samples of the plurality of training samples. The method can include, for one or more second training iterations, training, based at least in part on a second regularization magnitude greater than the first regularization magnitude, the machine-learned model using one or more respective second training samples of the plurality of training samples.

Abstract: A patient interface for supplying a flow of breathable gas to the airways of a patient may comprise a heat and moisture exchanger (HME). The HME may be positioned in a flow path of the flow of breathable gas. The HME may absorb heat and moisture from gas exhaled by the patient and the incoming flow of breathable gas to be supplied to the patient's airways may be heated and moisturized by the heat and moisture held in the HME.

Abstract: A processor obtains pieces of coded data, which are included in a bitstream and generated by coding tiles, and tile boundary independence information, which indicates whether each boundary between the tiles is a first or second boundary. Image data of a first tile is generated by decoding a first code string included in first coded data with reference to decoding information of a decoded tile when the tile boundary independence information indicates the first boundary, and by decoding the first code string without referring to the decoding information when the tile boundary independence information indicates the second boundary. A bit string is added after the first code string to make a bit length of first coded data a multiple of a predetermined N bits, with N being an integer greater than or equal to 2.

Abstract: A maintenance method for servicing a component includes generating, via an electronic control unit, a digital model of the component, detecting, via the electronic control unit, a defect of the component, classifying, via the electronic control unit, the defect as a defect type, and evaluating the defect to determine, according to the defect type, whether to repair the defect or to replace a section of the component containing the defect. A maintenance system for servicing components may include a scanner and an ECU configured to create a digital model of a component, detect a defect of the component from the digital model and/or three-dimensional data of the component, classify the defect as a defect type of a plurality of defect types, and/or evaluate the defect according to the defect type.

Abstract: The presently disclosed subject matter relates to an eye tracking method and device. The method can include receiving image data indicative of at least two images of a user's eye, identifying, in each image, regions related to the eye limbus; determining geometrical representation of the limbus structure, and determining a three-dimensional position and gaze direction of the user's eye (i.e. full six degrees of freedom of the eye) by triangulation of the geometrical representation of the limbus structure of at least two images.

Abstract: A street greening quality detection method based on physiological activation recognition is provided.

Abstract: Systems and methods in accordance with embodiments of the invention are configured to render images using light field image files containing an image synthesized from light field image data and metadata describing the image that includes a depth map. One embodiment of the invention includes a processor and memory containing a rendering application and a light field image file including an encoded image, a set of low resolution images, and metadata describing the encoded image, where the metadata comprises a depth map that specifies depths from the reference viewpoint for pixels in the encoded image. In addition, the rendering application configures the processor to: locate the encoded image within the light field image file; decode the encoded image; locate the metadata within the light field image file; and post process the decoded image by modifying the pixels based on the depths indicated within the depth map and the set of low resolution images to create a rendered image.

Abstract: Disclosed are a positioning method, an electronic device, and a storage medium. The method includes: converting, according to an intrinsic parameter matrix of an image acquisition module and depth values of multiple pixel points in a depth image captured by the image acquisition module, pixel coordinates of the multiple pixel points into camera coordinates; and matching the camera coordinates of the respective pixel points with target world coordinates of multiple voxels in a pre-established point cloud map, and obtaining a positioning result of the image acquisition module; wherein the target world coordinates of the respective voxels are obtained by updating initial world coordinates of the respective voxels according to multiple sample image pairs, and a sample image pair comprises a two-dimensional sample image and a depth sample image.

Abstract: The present disclosure provides systems and methods for predicting a disease state of a subject using ultrasound imaging and ancillary information to the ultrasound imaging. At least two quantitative measurements of a subject, including at least one measurement taken using ultrasound imaging, as part of quantified information can be identified. One of the quantitative measurements can be compared to a first predetermined standard, included as part of ancillary information to the quantified information, in order to identify a first initial value. Further, another of the quantitative measurements can be compared to a second predetermined standard, included as part of the ancillary information, in order to identify a second initial value. Subsequently, the quantitative information can be correlated with the ancillary information using the first initial value and the second initial value to determine a final value that is predictive of a disease state of the subject.

Abstract: An example system includes a processor to receive a set of features, a set of relations between the features, and a set of target features. Each of the target features is associated with a number of the relations. The processor can generate a hypergraph based on the features and the relations. The processor also can select a subset of features based on a transitive closure of the hypergraph for each of the target features. The processor can transmit the selected subset of features.

Abstract: An embodiment generates an initial set of training data from monitoring data. The initial set of training data is generated by combining outputs from a plurality of pretrained classifiers. The embodiment trains a new classification model using the initial set of training data to identify anomalies in monitoring data. The embodiment performs a multiple-level clustering of the data samples resulting in a plurality of clusters of sub-clusters of data samples, and generates a review list of data samples by selecting a representative data sample from each of the clusters. The embodiment receives an updated data sample from the expert review that includes a revised target classification for at least one of the data samples of the expert review list. The embodiment then trains another replacement classification model using a revised set of training data that includes the updated data sample and associated revised target classification.

Abstract: A computing device, method and computer program product are provided to generate synthetic images of abnormalities on the surface of an object, such as a vehicle. The synthetic images of abnormalities on the surface of an object may be utilized for training a machine learning algorithm to detect and/or classify abnormalities. In the context of a method, a respective abnormality is parametrically modeled by selecting one or more control points that satisfy parameters associated with the respective abnormality and generating a surface representative of the respective abnormality based on the one or more control points. The method also renders a synthetic image of at least a portion of the surface of the object having the respective abnormality as defined by the parametric modeling thereof. The rendering of the synthetic image includes rendering the synthetic image in accordance with a predefined lighting condition and from a predefined viewpoint.

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for improved image segmentation using hyperspectral imaging. In some implementations, a system obtains image data of a hyperspectral image, the image data comprising image data for each of multiple wavelength bands. The system accesses stored segmentation profile data for a particular object type that indicates a predetermined subset of the wavelength bands designated for segmenting different region types for images of an object of the particular object type. The system segments the image data into multiple regions using the predetermined subset of the wavelength bands specified in the stored segmentation profile data to segment the different region types. The system provides output data indicating the multiple regions and the respective region types of the multiple regions.

Abstract: An image encoder is provided including circuitry and a memory coupled to the circuitry. The circuitry, in operation, responds to a size of a block satisfying a size condition by generating a prediction image using a prediction mode selected from a plurality of prediction modes. The plurality of prediction modes include a first prediction mode in which a prediction process uses a motion vector and a reference block in a same picture as the block. The circuitry encodes the block using the prediction image.

Abstract: A processor performs machine learning, which uses a radiographic image that does not include a suture needle as a surgical tool and a surgical tool image that includes the suture needle in a posture different from a linear posture as training data, to construct a trained model for detecting a region of the suture needle from an input radiographic image.