Koh Young Technology Inc.

Jin Man Kang

Filip Lukasz Piekniewski

Dae Sung Koo

Woo Young Lim

Joong Ki Jeong

Ki Won Park

Seung Bum Han

Jung Ho Son

Min Cheol Yoon

Kenneth Mark Sullivan

Se Rin Lee

Jaedu Cho

Xing Zheng

A method for processing one or more histological images captured by a medical imaging device is disclosed. In this method, the histological image is received, and target regions each of which corresponds to a candidate type of tissue are identified based on a predictive model associating one more sample histological images with one or more sample target histological images. One or more display characteristics associated with the identified at least one target histological image is applied to the histological image.

Method and apparatus for processing histological image captured by medical imaging device

A method, performed by a computer, for volumetrically determining a mid-sagittal plane (MSP) from a plurality of magnetic resonance (MR) images of a brain is disclosed. The plurality of MR images is received and converted into a 3D volume image defined in a 3D coordinate space. Semantic segmentation of an anterior commissure (AC) region, a posterior commissure (PC) region, and a third ventricle region in the 3D volume image is performed based on a pre-trained segmentation model to generate a 3D mask image labeled with the AC region, the PC region, and the third ventricle region. Based on the labeled 3D mask image, a 3D volumetric image of the segmented third ventricle region is constructed. The MSP is determined based on the 3D volumetric image of the third ventricle region.

Method and apparatus for determining mid-sagittal plane in magnetic resonance images

There is provided a technique that includes: a camera configured to capture images of the target object; a memory configured to store the images of the target object and feature data including one or more predetermined exterior features of the target object; and a processor configured to: determine a first process configuration for an operation including a plurality of image processes; perform the operation under the first process configuration; generate inspection data from the sets of images that have been processed; generate an inspection score by comparing the inspection data with the feature data; compare the inspection score with a predetermined threshold score; set the first process configuration as an optimal configuration if the inspection score satisfies the predetermined threshold score.

Apparatus for optimizing inspection of exterior of target object and method thereof

An apparatus for inspecting components may include a processor for: determining exterior information of a first component mounted on a first printed circuit board; inspecting a mounting state of the first component by using inspection information of a second component having a first similarity value, when the first similarity value is higher than or equal to a preset reference similarity value, and updating the inspection information of the plurality of components by using the inspection information of the first component matched with the inspection information of the second component, when it is determined that the mounting state of the first component is good.

Apparatus for inspecting components mounted on printed circuit board, operating method thereof, and computer-readable recording medium

A printed circuit board inspection apparatus can inspect the mounting state of a component by generating depth information on the component mounted on a printed circuit board by using a pattern of light reflected from the component and received by an image sensor, inputting the generated depth information into a machine-learning-based model, obtaining depth information with reduced noise from the machine-learning-based model, and using depth information with reduced noise.

Method for inspecting mounting state of component, printed circuit board inspection apparatus, and computer readable recording medium

A printed circuit board inspection apparatus can inspect a mounting state of a component by generating depth information on the component by using a pattern of light reflected from the component mounted on a printed circuit board received by an image sensor, generating two-dimensional image data for the component by using at least one of light of a first wavelength, light of a second wavelength, light of a third wavelength, and light of a fourth wavelength reflected from the component received by a first image sensor, inputting the depth information and the two-dimensional image data for the component into a machine learning-based model, obtaining depth information with reduced noise from the machine learning-based model, and using the noise-reduced information.

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San Diego, CA, United States of America

Jin Man Kang