Abstract: An endoscope system and an operation method therefor in which information regarding a residual liquid is obtained to efficiently remove the residual liquid are provided. On the basis of image signals in a plurality of wavelength ranges, baseline information that is information regarding a light scattering characteristic or a light absorption characteristic of an observation target and that does not depend on specific biological information is calculated. A baseline correction value for adjusting the baseline information to reference baseline information that is a reference for evaluating the baseline information is calculated. From the baseline correction value, residual liquid information regarding a residual liquid included in the observation target is calculated.

Abstract: Systems and methods for driving a flexible medical instrument to a target in an anatomical space with robotic assistance are described herein. The flexible instrument may have a tracking sensor embedded therein. An associated robotic control system may be provided, which is configured to register the flexible instrument to an anatomical image using data from the tracking sensor and identify one or more movements suitable for navigating the instrument towards an identified target. In some embodiments, the robotic control system drives or assists in driving the flexible instrument to the target.

Abstract: The disclosure relates to digital twin of cardiovascular system called as cardiovascular model to generate synthetic Photoplethysmogram (PPG) signal pertaining to disease conditions. The conventional methods are stochastic model capable of generating statistically equivalent PPG signals by utilizing shape parameterization and a nonstationary model of PPG signal time evolution. But these technique generates only patient specific PPG signatures and do not correlate with pathophysiological changes. Further, these techniques like most synthetic data generation techniques lack interpretability. The cardiovascular model of the present disclosure is configured to generate the plurality of synthetic PPG signals corresponding to the plurality of disease conditions. The plurality of synthetic PPG signals can be used to tune Machine Learning algorithms. Further, the plurality of synthetic PPG signals can be utilized to understand, analyze and classify cardiovascular disease progression.

Abstract: A machine learning system may automatically produce classifier algorithms and configuration parameters by selecting them into a set of predetermined unitary algorithms and associated parametrization values. Multiple digital representations of input object items may be produced by varying the position and orientation of the object to be classified and/or of the sensor to capture a digital representation of the object, and/or by varying a physical environment parameter which changes the digital representation capture of the object by the sensor. A robot arm or a conveyor may vary the object and/or the sensor positions and orientations. The machine learning system may employ generic programming to facilitate the production of classifiers suitable for the classification of multiple digital representations of input object items.

Abstract: A location estimation apparatus includes a reliability calculation circuit, a selection circuit, and a self-location estimation circuit. The reliability calculation circuit calculates a reliability with respect to location estimation for a plurality of devices capturing a plurality of input images. The selection circuit selects one of the devices based on the reliability. The self-location estimation circuit performs self-location estimation based on the image captured by the selected device.

Abstract: Aspects of the subject disclosure may include, for example, systems and methods aggregating video content and adjusting the aggregate video content according to a training model. The adjusted aggregate video content comprises a first subset of the images and does not comprise a second subset of the images. The first subset of the images is determined by the training model based on a plurality of categories corresponding to a plurality of events. The illustrative embodiments also include presenting the adjusted aggregate video content and receiving identifications for the first subset of the images in the aggregate video content. Further, the illustrative embodiments include adjusting the training model according to the identifications and providing the adjusted training model to a network device. Other embodiments are disclosed.

Abstract: An image processing device includes: a determination unit configured to determine, based on a feature amount of input image data, a category of the input image data and a score representing confidence of the category, with a classification model; a display unit configured to display an image representing the input image data and the determined category of the input image data; an acceptance unit configured to accept a correction of the displayed category from a user; and an updating unit configured to update the classification model, based on the correction of the category.

Abstract: Systems and methods for operating radar systems. The methods comprise, by a processor: receiving point cloud information generated by radar devices; grouping data points of the point cloud to form at least one segment; computing possible true range-rate values for each data point in the at least one segment; identifying a scan window including possible true range-rate values for a largest number of data points; determining whether at least two modulus of the data points associated with the possible true range-rate values included in the identified scan window have moduli values that are different by a certain amount; determining a new range-rate value for each data point of the segment, when a determination is made that at least two modulus of the data points do have moduli values that are different by the certain amount; and modifying the point cloud information in accordance with the new range-rate value.

Abstract: Systems and methods are disclosed for using spatial filter to reduce bundle adjustment block size, including a method comprising: assigning a plurality of feature tracks to a voxel corresponding to a region of a geographic area, the voxel having a length, a width and a height, each feature track including a geographic coordinate within the region, a first image identifier identifying a first image, a second image identifier identifying a second image, a first pixel coordinate identifying a first location of a first feature in the first image, and a second pixel coordinate identifying a second location of the first feature within the second image; determining a quality metric value of the feature tracks assigned to the voxel; and conducting bundle adjustment on a subset of the feature tracks assigned to the voxel, the subset of the feature tracks based on the quality metric value.

Abstract: This disclosure is directed to using cascading algorithms to automatically identify items placed in a tote or other receptacle utilized by users in material handling facilities as the users move around the facilities. A tote may store a database or “gallery” of item representations for all of the items that are stored in the facility that a user may place in their totes. The tote may use multiple algorithms in a cascading manner to analyze the gallery of item representations in order to iteratively narrow the search space of item representations in the gallery to determine which of the items was placed in the tote by a user. Upon identifying the item placed in the tote, the tote may add an item identifier for the item to a virtual listing of item identifiers representing items previously placed in the tote.

Abstract: A processor-implemented method is disclosed. The method includes: receiving, from a first client device, a signal representing image data depicting at least one first document containing a product specification for a first product; performing text recognition on the image data to identify text in the at least one first document; determining at least one first value associated with the first product based on the recognized text; identifying a second product based on determining that product specification for the second product satisfies one or more predetermined criteria relating to the at least one first value; determining at least one second value associated with the second product; generating first display data including a graphical representation of the at least one second value; and transmitting, to the first client device via a communications module, a signal representing the first display data.

Abstract: The present invention relates to a computer-implemented method for improving the interpretation of the surroundings of a vehicle (100), wherein the method comprises: receiving a source image (14) of a surrounding environment (118) of the vehicle, which source image is captured by a sensor unit (12a) of the vehicle; receiving or computing a depth data image (20) comprising depth data based on the source image and at least one more source image; detecting a repetitive pattern (30) in the received source image; and based on the detection of the repetitive pattern, determining that an area (32) of the depth data image, which area (32) corresponds to an area (34) of the detected repetitive pattern in the received source image, contains unreliable depth data.

Abstract: The present application relates to methods, apparatuses, computer devices, and storage mediums for generating an aerial photographing path for a UAV. A method according to an embodiment includes acquiring an inputted aerial photographing landmark; obtaining a UAV aerial photographing safe space according to the aerial photographing landmark; constructing a viewing angle quality scalar field of the aerial photographing landmark; and generating an aerial photographing path set in the UAV aerial photographing safe space according to the viewing angle quality scalar field.

Abstract: A computer includes a processor and a memory, the memory storing instructions executable by the processor to determine respective probabilities of a direction of a gaze of a vehicle occupant toward each of a plurality of points in an image, determine a gaze distance from a center of the image based on the probabilities, and, upon determining that the gaze distance exceeds a threshold, suppress manual control of at least one vehicle component.

Abstract: A method and a system for determining a 6-DOF-pose of an object in space use at least one marker attached to the object and a plurality of cameras. Perspectives of the cameras are directed to a common measuring space in which the object is positioned. At least one camera from the plurality of cameras is movable such that the movable camera can be adjusted with respect to the object. At least one of the cameras captures an image of said marker attached to the object. Based on the at least one captured image, spatial parameters representing the 3D-position or the 6-DOF-pose of the marker and, consequently, the 6-DOF-pose of the object in space can be determined.

Abstract: A method for detecting and classifying defects in high-throughput transparent articles such as syringes, vials, cartridges, ampules, and bottles is provided. The method includes the steps of providing a stream of the articles; capturing a first digital image of each of the articles in the stream; inspecting the first digital image for objects; determining parameters of the objects; performing a first classification step to classify the objects into a first defect class and a second defect class; performing a second classification step to classify the objects into a plurality of defect types using at least two second classification models; comparing at least one object parameter of a classified object with a predetermined defect type dependent threshold; classifying the article as defective or non-defective based on the comparing step; and separating defective articles from non-defective articles.

Abstract: Receive first context information (FCI) including entered in-field incident timeline information values from an in-field incident timeline application and a time associated with an entry of the FCI values. Access a mapping that maps in-field incident timeline information values to events having a pre-determined threshold confidence of occurring and identify an event associated with the received FCI. Determine a location associated with the entry of the FCI and a time period associated with the entry of the FCI. Access a camera location database and identify cameras that have a field of view including the location during the time period. Retrieve audio and/or video streams captured by the cameras during the time period. And provide the audio and/or video streams to machine learning training modules corresponding to machine learning models for detecting the event in and/or video streams for further training of the machine learning models.

Abstract: A system and method for producing an image is provided in which magnitude data related to photo data received from multiple apertures configured with photo detectors is manipulated to conform to an assumed image, phase data related to the photo data received from multiple apertures configured with photo detectors is multiplied by the manipulated magnitude resulting in an image function, imaging constraints are applied to the Fourier transform of the image function to create a desired image, and the desired image is tested to determine whether additional iterations of the method are necessary, with the desired image becoming the assumed image in subsequent iterations, until the desired image is no long substantially different from the assumed image.

Abstract: Method, apparatus, and computer program product are provided for estimating crop type and/or sowing date. In some embodiments, a historical crop growth time series and a plurality of simulated crop growth time series are determined, and the historical time series is matched against each simulated time series to determine an estimated crop type and/or sowing date. For example, one simulated time series may be determined for each crop type/sowing date combination within a set of one or more crop types and one or more sowing dates based on historical crop data. Each time series represents crop growth in an area of interest and comprises element(s) including crop-specific parameter(s), such as leaf area index (LAI). The historical time series may be determined based on remote sensor data. Each simulated time series may be determined using a crop growth simulation model and based on historical crop data, geospatial data, and weather data.

Abstract: The present invention is directed to positioning a plurality of eye feature points in the target image to determine position coordinates of the plurality of eye feature points normalizing the position coordinates of the plurality of eye feature points to obtain normalized position feature data and determining an eye state in the target image based on the position feature data.