A detector (110) for determining at least one material property of at least one object (112) is proposed. The detector (110) comprises at least one projector (116) configured for illuminating the object (112) with at least one illumination pattern (118) comprising a plurality of illumination features (120); at least one first camera (122) having at least one first sensor element, wherein the first sensor element has a matrix of first optical sensors, the first optical sensors each having a light-sensitive area, wherein each first optical sensor is designed to generate at least one sensor signal in response to an illumination of its respective light-sensitive area by a reflection light beam propagating from the object (112) to the first camera (122), wherein the first camera (122) is configured for imaging at least one first reflection image comprising a plurality of first reflection features generated by the object (112) in response to illumination by the illumination features (120), wherein the first camera (122) is arranged such that the first reflection image is imaged under a first direction of view to the object (112); at least one second camera (124) having at least one second sensor element, wherein the second sensor element has a matrix of second optical sensors, the second optical sensors each having a light-sensitive area, wherein each second optical sensor is designed to generate at least one sensor signal in response to an illumination of its respective light-sensitive area by a reflection light beam propagating from the object (112) to the second camera (124), wherein the second camera (124) is configured for imaging at least one second reflection image comprising a plurality of second reflection features generated by the object (112) in response to illumination by the illumination feature (120), wherein the second camera (124) is arranged such that the second reflection image is imaged under a second direction of view to the object (112), wherein the first direction of view and the second direction of view differ; at least one evaluation device (126) configured for evaluating the first reflection image and the second reflection image, wherein the evaluation comprises matching the first reflection features and the second reflection features and determining a combined material property of matched pairs of first and second reflection features by analysis of their beam profiles.

An image classification system and method are used to determine the status of equipment completeness of the rental two-wheeled vehicles, such as electric scooters. The system and method use deep learning models to analyze and classify ambiguous states of the rental vehicle when the user finishes the ride. These states are likely to be encountered by rental vehicles, to protect helmets, trunks or other equipment from loss or damage.

A medical information processing apparatus according to an embodiment includes a storage and processing circuitry. The storage is configured to store therein, for each point of a frequency space represented by a plurality of pieces of first frequency component data acquired by applying frequency conversion to data inside regions of interest set to medical images, characteristic data representing a tendency of spectral values that appear at the point. The processing circuitry is configured to acquire second frequency component data by applying frequency conversion to a medical image to be processed, to determine similarity of a spectral value at each point of a frequency space represented by the second frequency component data, with the characteristic data, and to designate a target area in the frequency space represented by the second frequency component data based on the result of the determination.

A method is disclosed that includes receiving, by a processing device, a plurality of images of a test assembly. The processing device selects a component in the test assembly and an image of the plurality of images of the test assembly as received. For the component as selected and the image as selected, the processing device compares a plurality of portions of the image as selected to a corresponding plurality of portions of a corresponding profile image and computing a matching score for each of the plurality of portions. The processing device selects a largest matching score from the matching score for each of the plurality of portions as a first matching score for the component as selected and the image as selected. The first matching score is stored for the component as selected and the image as selected.

An image similarity determination apparatus includes a memory and a processor configured to acquire a first image and a second image, perform selection of a first group and a second group from a plurality of feature points included in the first image and perform selection of a third group and a fourth group from a plurality of feature points included in the second image, calculate feature quantity for each feature point included in the first group and the third group on the basis of luminance and calculate feature quantity for each feature point included in the second group and the fourth group on the basis of hue, and determine similarity between the first image and the second image on the basis of both first comparison of the first group with the third group and second comparison of the second group with the fourth group.

A similar picture identification method is described. According to the method, processing circuitry of a device obtains n first local regions of a first picture, the n first local regions being a first set of regions that is affine invariant, and obtains m second local regions of a second picture, the m second local regions being a second set of regions that is affine invariant. The processing circuitry obtains n first characteristic values respectively corresponding to the n first local regions, and obtains m second characteristic values respectively corresponding to the m second local regions. The processing circuitry further determines, according to a comparison result of the n first characteristic values and the m second characteristic values, whether the first picture is similar to the second picture, where n and m are positive integers.

An ultrasound observation apparatus includes: an agreement determination circuit configured to compare a reference image that is an ultrasound image chosen from ultrasound images and in which at least one region of interest is set and a latest ultrasound image with each other at least partly, and determine whether the reference image and the latest ultrasound image agree with each other; and a measurement circuit configured to, when the agreement determination circuit determines that the reference image and the latest ultrasound image agree with each other, transmit a push pulse to the at least one region of interest to cause shear waves, transmit and receive a tracking pulse to detect propagation of the shear waves, and measure elasticity characteristics in the at least one region of interest.

An orientation device, an orientation system and an orientation method are provided. The orientation device includes a seat body, a pressure sensor, and a computing unit. The seat body includes a bearing surface, and the seat body is non-directional. The pressure sensor is disposed below the bearing surface. The pressure sensor is configured to obtain a plurality of pressure data of the bearing surface when an object is disposed on the bearing surface. The computing unit is coupled to the pressure sensor. The computing unit is configured to analyze the pressure data to obtain a direction data. The direction data is configured to determine a first direction of the seat body.

A method of label detection includes: obtaining, by an imaging controller, an image depicting a shelf; increasing an intensity of a foreground subset of image pixels exceeding an upper intensity threshold, and decreasing an intensity of a background subset of pixels below a lower intensity threshold; responsive to the increasing and the decreasing, (i) determining gradients for each of the pixels and (ii) selecting a candidate set of the pixels based on the gradients; overlaying a plurality of shelf candidate lines on the image derived from the candidate set of pixels; identifying a pair of the shelf candidate lines satisfying a predetermined sequence of intensity transitions; and generating and storing a shelf edge bounding box corresponding to the pair of shelf candidate lines.

Example implementations include a method, apparatus and computer-readable medium for computer vision detection of a fall event, comprising detecting a person in a first image captured at a first time. The implementations further include identifying a plurality of keypoints on the person in an image, wherein the plurality of keypoints, when connected, indicate a pose of the person. Additionally, the implementations further include detecting, using the plurality of keypoints, that the person has fallen in response to determining that, subsequent to the pose being the standing pose in a previous image, the keypoints of the plurality of keypoints associated with the shoulders of the person are higher than the keypoints of the second plurality of keypoints associated with the eyes and the ears of the person in the second image. Additionally, the implementations further include generating an alert indicating that the person has fallen.