Abstract: An image analysis device includes a person detecting unit for detecting person images from image information from a surveillance camera, and acquiring both pieces of person image information as a result of the detection of person images, and pieces of attribution information about persons corresponding to the pieces of person image information, and a person narrowing down unit for performing narrowing-down of person image information with respect to the pieces of person image information and the pieces of person attribution information which are acquired by the person detecting unit, on the basis of a person narrowing down rule, to acquire person image information after the narrowing-down and attribution information about a person corresponding to the person image information.

Abstract: An information processing apparatus constructs a virtual space to express a subject by acquiring an image obtained by observing a reality space in which one or more subjects are present, and arranging a plurality of unit volume elements at positions that are determined depending on a subject captured in the acquired image, and the information processing apparatus constructs a virtual space by arranging multiple types of unit volume elements with different sizes.

Abstract: An obstacle recognition device of a vehicle provided with a camera capturing an image around the vehicle, includes an acquiring unit sequentially acquiring the image captured by the camera; a feature point extracting unit extracting a plurality of feature points of an object included in the image; a calculation unit calculating each motion distance of the plurality of feature points between the image previously acquired and the image currently acquired by the acquiring unit; a first determination unit determining whether each motion distance of the feature points is larger than or equal to a first threshold; a second determination unit determining whether each motion distance of the feature points is larger than or equal to a second threshold; and an obstacle recognition unit recognizing an obstacle.

Abstract: A system for identification of bacterial cells in free solution in a sample. A sample handler is adapted to position the sample. A light source illuminates a large volume of the sample. An imager is located to receive light scattered from the sample. A computer it is coupled to receive data transmitted from the imager. A controller is coupled to send control signals to the sample handler and the computer. The imager processes the scattered light to form images of the bacteria and transmits bacteria image information to the computer, wherein the bacteria image information includes intensity values and position data for the bacteria images from which the computer determines the presence of bacteria.

Abstract: An information processing apparatus includes a processor configured to execute first acquisition processing for acquiring a first recognition result and a first recognition probability on target data from a first recognizer, execute second acquisition processing for acquiring a second recognition probability for the first recognition result on the target data from a second recognizer, and execute control for determining which of first processing and second processing with a necessary human workload greater than in the first processing is to be executed for the first recognition result based on the first recognition probability and the second recognition probability.

Abstract: A method for enhancing a set of object scan images may be provided. The set of object scan images comprises at least two scan images. The method comprises determining first distance and determining an interpolated scan image, by applying an interpolation algorithm comprising determining any pixel of the interpolated scan image as a white pixel if the corresponding pixels on the scan images are both white. The same applies to black pixels. The method may further comprise determining any pixel which corresponding pixels are black on one of the two image scans and white on the other image scans as white (black) if a predefined percentage of the directly surrounding pixels is white (black) in the interpolated scan image. Furthermore, the method comprises inserting the interpolated scan image between the first scan image and the second scan image and repeating the above steps until a stop condition is met.

Abstract: A light, such as an ultraviolet (UV) light, is used with a scanning test device in a scanning process of one or more of a user's hands to assess hand cleanliness according to a medical pre-treatment protocol for a medical treatment device, such as for a dialysis treatment using a home dialysis machine. If the scanning process results in a determination that the hands do not pass a cleanliness standard, the user is requested to perform a cleaning protocol and re-perform a scanning test. The scanning test device may be a smartphone, tablet device and/or other smart device running a software app that obtains and scans an image of the user's hand(s) illuminated by the UV light and makes an assessment of hand cleanliness according to an assessment algorithm. After passing the scanning test, the scanning test device signals to the treatment machine that the medical pre-treatment protocol may continue.

Abstract: A method and an apparatus for recognizing a descriptive attribute of an appearance feature include obtaining a location feature of an appearance feature of a target image to determine a location of a part of an object in a preset object model indicated by the appearance feature, where the location feature of the appearance feature indicates the location of the part of the object in the preset object model indicated by the appearance feature, recognizing a target region based on the location feature, where the target region includes the part of the object, performing feature analysis on the target region, recognizing a descriptive attribute of the appearance feature of the object, and determining the location feature of the appearance feature having a local attribute.

Abstract: Provided are a method and an apparatus wherein an object is distinguished from the background of the object by using a sensor unit including at least one sensor, the distances to measurement points of the background and/or the object are measured, and information on the shape of at least one part constituting the object is acquired on the basis of the measured distances.

Abstract: An operating assistance method for a working device or for a vehicle. Object boxes for an object in a field of view of the working device are obtained at consecutive times. From object boxes of a given object, for images recorded in succession or direct succession, an instantaneous change of scale of an object box for the specific object, and an instantaneous lateral change of position of the object box for the specific object, are determined. An object box predicted in the future is determined from the current change of scale, and from the instantaneous lateral change in position for an object box for a specific object. The position of the predicted object box and/or the ratio of a lateral extension of the predicted object box to a lateral extension of a covered field of view and/or of the recorded images are determined and evaluated.

Abstract: A method, system and computer readable storage media for segmenting individual intra-oral measurements and registering said individual intraoral measurements to eliminate or reduce registration errors. An operator may use a dental camera to scan teeth and a trained deep neural network may automatically detect portions of the input images that can cause registration errors and reduce or eliminate the effect of these sources of registration errors.

Abstract: A system, including a processor and a memory, the memory including instructions to be executed by the processor to identify first object features from sensor data acquired by a stationary sensor at a first time step, determine second object features at a second time step. The instructions can include further instructions to determine one or more object clusters of first object features by determining distances measured in pixels between the first object features and corresponding second object features and comparing the distances to one or more mean distances and determine one or more object groups of inlier first object features in the one or more object clusters by determining a plurality of similarity transformations for a plurality of random samples of first object features and determining inlier first object features based on maximizing the number of first object features included in a similarity transformation.

Abstract: To identify and track a plurality of objects. An object tracking device 1 includes a frame acquisition unit 31 for acquiring one set of frame data 11 included in moving image data that captures a space where a plurality of objects are present; a distance acquisition unit 32 for acquiring measurement data 12 from measurement of three-dimensional coordinates of points configuring the objects in the space where the objects are present; an object position detection unit 33 for detecting a two-dimensional position of each of the objects from the frame data 11 on the basis of comparison with template data of each of the objects; an object distance detection unit 34 for detecting a plurality of three-dimensional positions of the objects from the measurement data 12; and an associating unit 35 for associating each two-dimensional position from the object position detection unit 33 with the nearest three-dimensional position among the three-dimensional positions from the object distance detection unit 34.

Abstract: Various embodiments of present invention provide systems, methods, and computer-program products containing executable code for processing a package for shipping with a common carrier. Various embodiments include a wireless computing device that may be one of several types of devices such as smartphones, mobile telephones, mobile computers, portable digital assistants, laptop computers, gaming devices, electronic tablets, or other types of similar devices that may be used to facilitate the shipping and expedited tracking of objects having a tracking number via multimedia capture and processing.

Abstract: An image recognition system includes a cloud server that stores a cloud recognition model and a local server that stores a local recognition model. The local server recognizes an image through the local recognition model to generate a local recognition result. The image corresponds to a field, the image includes at least one object, and the local recognition result includes local category confidence scores of each object corresponding to each of a plurality of categories. For each object, the local server calculates a local entropy indicator according to the field and the corresponding local category confidence scores. The local server determines to transmit the image to the cloud server according to the local entropy indicators of the image. The cloud server recognizes the image through the cloud recognition model to generate a cloud recognition result, and updates the local recognition model according to the cloud recognition result.

Abstract: A particle velocimetry system and algorithm are provided for executing a particle reconstruction to determine three-dimensional positions of particles in a particle laden fluid flow from two-dimensional flow images generated by two-dimensional image sensors; generate a three-dimensional particle distribution from the three-dimensional position; and execute a recursive loop for performing further iterations of particle reconstruction and generation of three-dimensional particle distributions, with recursive iterations of particle reconstruction executed with the use of particle property data obtained from the prior executed iteration of particle reconstruction.

Abstract: An embodiment of the invention may include a method, computer program product and computer system for object detection and identification. The method, computer program product and computer system may include computing device which may receive an image from a user device. The image may be a screenshot captured by the user device from a display. The computing device may classify the image based on features present in the image and detect a salient object contained within the image. The computing device may identify the object in the image and one or more sources of the object in the image.

Abstract: A multi-task hierarchical machine learning model is configured to perform both a decision task to predict a decision result and an explanation task to predict a plurality of semantic concepts for explainability associated with the decision task, wherein a semantic layer of the machine learning model associated with the explanation task is utilized as an input to a subsequent decision layer of the machine learning model associated with the decision task. Training data is received. The multi-task hierarchical machine learning model is trained using the received training data.

Abstract: A radiation image processing device (100) includes an image processor (6) configured to calculate an adjustment coefficient (Iideal/Ireal) for adjusting a signal reduction rate (Dscat) due to removal of a scattered component in a subject (S) of radiation transmitting through the subject (S) acquired based on a luminance value (Inogrid) of a radiation image (Xnogrid) captured with the radiation radiated to the subject (S) without a grid (8) being arranged, the luminance value (Inogrid) of the radiation image (Xnogrid) containing a signal of a scattered ray.

Abstract: The method includes: determining a reference sensor from at least two sensors and sampling moments of the reference sensor; when obtaining observation data of a sensor K at a moment t1, determining a type of the sensor K; if the sensor K is a first sensor of a first type, determining a sampling moment T1 that is closest to t1 and an interpolation coefficient ?1, and calculating first rotation data and first translation data that are at T1, and a first covariance matrix; if the sensor K is a second sensor of a second type, determining two sampling moments T2 and T3 that are closest to t1 and interpolation coefficients ?2 and ?3, and calculating second rotation data and second translation data that are of the second sensor from T2 to T3, and a second covariance matrix; merging rotation data, translation data, and covariance matrices that are at the sampling moments.