[Problem] To perform interpretation assistance of a back portion image in real time using a general-purpose computer for any case of scoliosis.

[Solution] An interpretation assistance apparatus includes a center line creating unit that creates a center line C in a back portion image of a subject, a measurement interval designating unit that accepts designation of an arbitrary measurement interval I along the center line C, a measurement width designating unit that accepts designation of an arbitrary measurement width W toward intersecting directions of the center line C, and a measurement point coordinate obtaining unit that obtains depth coordinates of respective measurement points that are apart from the center line on both the right and left sides by the measurement width W at every measurement interval I.

[Problem] To perform interpretation assistance of a back portion image in real time using a general-purpose computer for any case of scoliosis.

[Solution] An interpretation assistance apparatus includes a center line creating unit that creates a center line C in a back portion image of a subject, a measurement interval designating unit that accepts designation of an arbitrary measurement interval I along the center line C, a measurement width designating unit that accepts designation of an arbitrary measurement width W toward intersecting directions of the center line C, and a measurement point coordinate obtaining unit that obtains depth coordinates of respective measurement points that are apart from the center line on both the right and left sides by the measurement width W at every measurement interval I.

A system and a method for improved generation of 3D avatars for virtual try-on of garments is provided. Inputs from a first user type are received, via a first input unit, for generating one or more garment types in a graphical format. Further, a 3D avatar of a second user type is generated in a semi-automatic manner or an automatic manner based on capturing a first input type or a second input type respectively received via a second input unit. The first input type comprises measurements of body specifications of the second user type and the second input type comprises body images of the second user type. Further, the generated garments are rendered on the generated 3D avatar of the second user type for carrying out a virtual try-on operation.

The present disclosure relates to an image processing method, a storage medium and a mapping system. An imageological image including a plurality of tomographic images is acquired. A three-dimensional reconstruction is performed according to the plurality of tomographic images to obtain a three-dimensional image model. The three-dimensional image model includes a three-dimensional myocardial fibrosis region image. A three-dimensional electroanatomic model including a three-dimensional abnormal myocardial tissue image is acquired. Since there is a certain correlation between an abnormal myocardial tissue region in the three-dimensional electroanatomic model and myocardial fibrosis, the three-dimensional image model and the three-dimensional electroanatomic model are registered, and an overlapping part of the three-dimensional myocardial fibrosis region image and the three-dimensional abnormal myocardial tissue image is determined as the location of a lesion. Therefore, the accurate positioning of a lesion location is realized, which effectively improves the surgery success rate.

Highly accurate three-dimensional shape data is obtained easily. The information processing apparatus obtains data of a plurality of captured images obtained by capturing an object from a plurality of viewpoints different from one another. Further, the information processing apparatus obtains first three-dimensional shape data indicating the shape of the object, which includes information indicating the spatial position of each of a plurality of feature points indicating features of the object. Furthermore, the information processing apparatus obtains second three-dimensional shape data indicating the shape of the object by using the captured image. Here, the information processing apparatus obtains, at the time of obtaining the first three-dimensional shape data, the first three-dimensional shape data, based on reliability of the spatial position of each of the plurality of feature points and the second three-dimensional shape data.

An image processing apparatus includes at least one processor. The processor is configured to execute region-of-interest image generation processing of generating a region-of-interest image from a projection image, which is obtained at an irradiation position closest to a position facing a detection surface of a radiation detector, among a series of projection images obtained by irradiating a breast with radiations and imaging the breast, and shape type determination processing of determining a type of a shape of a calcification image included in the region-of-interest image generated by the region-of-interest image generation processing.

An image processing apparatus includes at least one processor. The processor is configured to execute region-of-interest image generation processing of generating a region-of-interest image from a projection image, which is obtained at an irradiation position closest to a position facing a detection surface of a radiation detector, among a series of projection images obtained by irradiating a breast with radiations and imaging the breast, and shape type determination processing of determining a type of a shape of a calcification image included in the region-of-interest image generated by the region-of-interest image generation processing.

Provided are a patch GAN-based depth completion method and apparatus in an autonomous vehicle. The patch-GAN-based depth completion apparatus according to the present invention comprises a processor; and a memory connected to the processor, wherein the memory stores program instructions executable by the processor for performing operations in a generating unit of a generative adversarial neural network comprising a first branch and a second branch based on an encoder-decoder comprising receiving an RGB image and a sparse image through a camera and LiDAR, generating a dense first depth map by processing color information of the RGB image through the first branch, generating a dense second depth map by up-sampling the sparse image through the second branch, generating a dense final depth map by fusing the first depth map and the second depth map, and determining, by a discriminating unit of the generative adversarial neural network, whether the final depth map is fake or real by dividing the final depth map and depth measurement data into a plurality of patches.

Provided are a patch GAN-based depth completion method and apparatus in an autonomous vehicle. The patch-GAN-based depth completion apparatus according to the present invention comprises a processor; and a memory connected to the processor, wherein the memory stores program instructions executable by the processor for performing operations in a generating unit of a generative adversarial neural network comprising a first branch and a second branch based on an encoder-decoder comprising receiving an RGB image and a sparse image through a camera and LiDAR, generating a dense first depth map by processing color information of the RGB image through the first branch, generating a dense second depth map by up-sampling the sparse image through the second branch, generating a dense final depth map by fusing the first depth map and the second depth map, and determining, by a discriminating unit of the generative adversarial neural network, whether the final depth map is fake or real by dividing the final depth map and depth measurement data into a plurality of patches.

Disclosed are a depth image processing method, a depth image processing apparatus (10), and an electronic device (100). The depth image processing method is applied in the electronic device (100) including a depth image capturing apparatus (20) configured to capture an initial depth image. The depth image processing method includes: obtaining (01) target depth data for a number of regions of interest of the initial depth image; determining (02) whether the number of regions of interest is greater than a predetermined value; grouping (03), in response to the number of regions of interest being greater than the predetermined value, the number of regions of interest based on the target depth data to obtain a target depth of field; obtaining (04) a target blurring intensity based on the target depth of field; and blurring (05) the initial depth image based on the target blurring intensity to obtain a blurred depth image.