A system for and a method of analyzing a corneal lesion using an anterior segment image according to the present invention. The system includes: an image acquisition unit configured to acquire an anterior segment image from the eyeball of a subject, a feature extractor configured to extract feature information on a position and a cause of a lesion in the cornea from the anterior segment image by applying a convolution layer to the anterior segment image through machine learning on the basis of a database in which clinical information pre-acquired by analyzing positions and causes of lesions in the corneas of subjects is stored; and a result determination unit configured to identify a position of the cornea from the anterior segment image using the feature information and to analyze and determine the position and the cause of the lesion in the cornea from the position of the cornea.

A system includes processing hardware and a memory storing software code. When executed, the software code receives first skeleton data including a first location of each of multiple skeletal key-points from the perspective of a first camera, receives second skeleton data including a second location of each of the skeletal key-points from the perspective of a second camera, correlates first and second locations of some or all of the multiple skeletal key-points to produce correlated skeletal key-point location data for each of at least some skeletal key-points. The software code further merges the correlated skeletal key-point location data for each of those at least some skeletal key-points to provide merged location data, and generates, using the merged location data and the locations of the first, second, and third cameras, a mapping of the 3D pose of a skeleton.

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.

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.

There is provided a computer implemented method for generating a three dimensional (3D) image based of fluorescent illumination, comprising: receiving in parallel by each of at least three imaging sensors positioned at a respective parallax towards an object having a plurality of regions with fluorescent illumination therein, a respective sequence of a plurality of images including fluorescent illumination of the plurality of regions, each of the plurality of images separated by an interval of time; analyzing the respective sequences, to create a volume-dataset indicative of the depth of each respective region of the plurality of regions; and generating a 3D image according to the volume-dataset.

A method for measuring objects in a digestive tract based on an imaging system is provided. The imaging system captures a detection image in the measurement stage. The depth distance z.sub.i from a target point P′ to a board is calculated, a correction factor is obtained according to the predicted brightness g.sup.−1(z.sub.i) of a reference point P in the detection image. The depth image z(x, y) from the actual position of each pixel to the board is calibrated by the correction factor. The scale r of each pixel is calculated according to the depth image z(x, y). The actual two-dimensional coordinates S.sub.i′ of each pixel are calculated by the scale r, and the actual three-dimensional coordinates (S.sub.i′, z(x, y)) of each pixel are obtained. The distance between any two pixels in the detection image or the area within any range are calculated.

An image segmentation apparatus for magnetic resonance imaging according to an embodiment includes processing circuitry. The processing circuitry is configured to obtain a localizer image of an organ, the localizer image being three-dimensional or being in a plurality of layers and two-dimensional. The processing circuitry is configured to temporarily localize, on a basis of the localizer image, a segment in which the organ is present in terms of the layer direction of a plurality of slices included in the localizer image. The processing circuitry is configured to obtain a segmentation result of the organ, by performing an image segmentation process on the localizer image positioned inside the segment in which the organ is present.

An antenna adjustment device and an antenna adjustment method for a mobile vehicle, in which main structure includes the mobile vehicle having a directional antenna, a photographic element and an epipolar line analysis component. The epipolar line analysis component establishes data connection with the directional antenna and the photographic element. The photographic element includes a photographic part and an image receiving processing part. With the above structure, the user can use the photographic element with the directional antenna to define an epipolar line of optimal connection efficiency with the base station on the image receiving processing part. When the connection is needed, the mobile vehicle is moved for adjusting an image of the base station imaged by the image receiving processing part to align to the epipolar line, so as to get the optimal connection efficiency.

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.

A data creation device that is a training data creation device for creating training data which is used for deep learning of an object detection program for detecting whether or not an object is included in an image, includes: an acquisition unit that acquires an anchor that is information on a frame specifying an area for each cell for detecting presence or absence of the object from the image; and a creation unit that associates area information of the object with image data to create the training data that includes a plurality of image data in which the area information is included. The creation unit determines a frame of the area information, based on a position of the anchor.