Samsung Electronics Co., Ltd.

Seoul National University

Yonsei University

Pusan National University

Inha-industry Partnership Institute

Ji-hye Kim

Min-Kyu Park

Sung-dae Cho

Yun-Je Oh

Jin-ho Kim

Hee-Chan Park

Min-Woo Lee

Jae-Sung Lee

Hyun-Hee Park

Moon-Gi Kang

Yong-sup Park

Chang-Won Kim

Kyoung-Tae Kim

Woo-hyun Nam

Seul-Ki Jang

Yeong-gil Shin

Choon-Woo Kim

Jin-wook Chung

Hyun-mook Oh

Il-Kyu Eom

Hyun-Meen Kang

Yong-geun Lee

A method of processing a medical image includes acquiring a three-dimensional (3D) medical image indicating a blood vessel and a two-dimensional (2D) medical image indicating the blood vessel, determining a blood vessel area in the 3D medical image corresponding to a partial area of the blood vessel in the 2D medical image, and matching the blood vessel area with the partial area in the 2D medical image.

Medical imaging device and medical image processing method

An image classification apparatus and method for Automatic White Balance (AWB) are provided. An input image is divided into blocks including pluralities of pixels. A hue value and a chroma value are calculated for each of the blocks. A color-changed block is detected by calculating, for each one of the blocks, differences between the hue and chroma values calculated for all blocks adjacent to the one of the blocks and the hue and chroma values calculated for the one of the blocks. A hue variance and a chroma variance are calculated for the entire input image if a number of the detected color-changed blocks is greater than or equal to a first threshold. The input image is determined as a non-monochromatic image, if the hue variance is greater than or equal to a second threshold or the chroma variance is greater than or equal to a third threshold.

Apparatus and method for classifying images

An image processing method and apparatus capable of easily detecting an edge of an object from an input image, in which the edge is detected using one step, without a pre-processing step, and a complicated trigonometric function is not used for gradient detection. The image processing method includes setting a window within an input image, analyzing the window to determine directions of edges of objects within the image included in the window, detecting edge information including the edge directions, and processing and outputting the window using the edge information.

Image processing method and apparatus for detecting an edge of an object within an image

A lens shading correction method and apparatus are provided for removing vignetting occurring in digital images due to lens shading. A white image captured by an image pickup device and an image pickup unit is separated into reference white images corresponding to color channels. A vignetting center having a maximum light intensity is estimated in each of the reference white images. Multiple reference segments on each of the reference white images are defined. A lens shading correction value corresponding to each pixel constituting the reference segments are calculated using a corresponding light intensity. A lens shading correction function corresponding to each reference segment is derived using a corresponding lens shading correction value. Vignetting of a general image received in a general image processing mode is removed, using the derived multiple lens shading correction functions.

Method and apparatus for correcting lens shading

A method and apparatus for estimating signal-dependent noise in a camera module are provided, in which an image is decomposed into high frequency components and low frequency components, a variance of noise in the image is estimated using the low frequency components, noise is eliminated from the high frequency components using the estimated noise variance, and the image is recovered using the noise-eliminated high frequency components and the low frequency components.

Apparatus and method for estimating signal-dependent noise in a camera module

Growing community of inventors

Goyang-si, South Korea

Ji-hye Kim