An apparatus comprising an interface, a structured light projector and a processor. The interface may receive pixel data. The structured light projector may generate a structured light pattern. The processor may process the pixel data arranged as video frames, perform operations using a convolutional neural network to determine a binarization result and an offset value and generate disparity and depth maps in response to the video frames, the structured light pattern, the binarization result, the offset value and a removal of error points. The convolutional neural network may perform a partial block summation to generate a convolution result, compare the convolution result to a speckle value to determine the offset value, generate an adaptive result in response to performing a convolution operation, compare the video frames to the adaptive result to generate the binarization result for the video frames, and remove the error points from the binarization result.

A method for automatically identifying global solar photovoltaic (PV) panels based on a cloud platform by using remote sensing. Optical images in a study area for a whole specific year are collected based on the cloud platform, and preprocessing is performed to obtain a surface reflectance image. Seven time-series images are derived and constructed based on spectral features of a solar PV panel: a solar PV panel index image, a water index image, a vegetation index image, a difference image between a first shortwave infrared band and a second shortwave infrared band, a difference image between the first shortwave infrared band and a near-infrared band, a blue band image, and a first shortwave infrared band image. Data in the seven time-series images are synthesized and reconstructed to obtain input data required by a model. A remote sensing theoretical model for automatically identifying a solar PV panel is constructed.

Disclosed is a method for recognizing a marine object based on hyperspectral data including collecting target hyperspectral data; preprocessing the target hyperspectral data; and detecting and identifying an object included in the target hyperspectral data based on a marine object detection and identification model, trained through learning of the detection and identification of the marine object. According to the present invention, the preprocessing and processing of the hyperspectral data collected in real time according to a communication state may be performed in the sky or on the ground.

An image processing apparatus includes a first image generator and a second image generator. The first image generator generates a first image, including a predetermined ruled-line image and an inscription image, from a second sheet in a sheet group. The sheet group is obtained by stacking multiple sheets including a single first sheet and the second sheet. The first sheet has inscription information inscribed thereon. The second sheet has the inscription image corresponding to the inscription information transferred thereon and includes the ruled-line image. The second image generator generates a second image in which a surplus image is removed from the first image generated by the first image generator in accordance with a learning model that has learned to remove the surplus image different from the ruled-line image and the inscription image.

In variants, the method for occlusion correction can include: determining a measurement depicting an occluded object of interest (OOI), optionally infilling the occluded portion of the object of interest within the measurement, and determining an attribute of the object of interest based on the infilled measurement.

Disclosed in the present invention are a method and apparatus for the identification of fetal cross-sections based on ultrasound dynamic images; the method includes: inputting sequentially each frame of fetal ultrasound images from acquired multiple consecutive frames of fetal ultrasound images into a predetermined feature-detecting model for analysis; acquiring a sequentially exported analysis from the feature-detecting model as feature information for each frame of fetal ultrasound images; corresponding to each frame of fetal ultrasound images, identifying a cross-section by the categories of the part and the structural feature. Obviously, for a fetal ultrasound image, the implementation of the present invention may improve the identified accuracy, identified efficiency, and the standardization of the cross-section, by acquiring the part features and structural features from consecutive multi-frame fetal ultrasound images and identifying the cross-section by combining the part features and structural features.

An image processing apparatus includes at least one processor. The processor is configured to execute calcification image detection processing of detecting a calcification image based on a plurality of tomographic images obtained from a series of a plurality of projection images obtained by tomosynthesis imaging of a breast, region-of-interest image group generation processing of generating a region-of-interest image group by cutting out, as a region-of-interest image, a region including the calcification image detected by the calcification image detection processing from each of the plurality of projection images, variance value calculation processing of calculating a variance value of feature amounts of each of the region-of-interest images included in the region-of-interest image group, and shape type determination processing of determining a type of a shape of the calcification image based on the variance value calculated by the variance value calculation processing.

Aspects and implementations of the present disclosure relate to filtering of return points from a point cloud based on radial velocity measurements. An example method includes: receiving, by a sensing system of an autonomous vehicle (AV), data representative of a point cloud comprising a plurality of return points, each return point comprising a radial velocity value and position coordinates representative of a reflecting region that reflects a transmission signal emitted by the sensing system; applying, to each of the plurality of return points, at least one threshold condition related to the radial velocity value of a given return point to identify a subset of return points within the plurality of return points; removing the subset of return points from the point cloud to generate a filtered point cloud; and identifying objects represented by the remaining return points in the filtered point cloud.

Methods and apparatuses for detecting characteristics of a card include a method for identifying a card containing foil, the foil detection method comprising converting an image file depicting a card into a hue, saturation, value (“HSV”) colour space, applying a value mask to exclude a group of pixels from analysis that do not form an intensified brightness area, and comparing the number of remaining pixels against a threshold to determine whether the card contains foil. In another aspect, a method for assigning a condition grading to a card comprises converting an image file depicting a diffusely illuminated card into an HSV colour space, isolating a uniform portion of the converted image file, applying a value mask to the converted image file to exclude pixels forming an undamaged portion of the card, and comparing the number of remaining pixels to a plurality of grading thresholds to assign a condition grading.

In variants, the method for occlusion correction can include: determining a measurement depicting an occluded object of interest (OOI), optionally infilling the occluded portion of the object of interest within the measurement, and determining an attribute of the object of interest based on the infilled measurement.