Welcome to GIP LAB

Computer Graphics & Image Processing Laboratory


about us.

We are a research laboratory working on graphics, computer vision and image processing.
In the field of graphics, we are studying the latest graphics library including OpenGL and DirectX in development. we also studying 3D modeling, VR, AR, and MR. If you study graphics, you can apply for 3D related companies, and you will be able to support VR and AR, which are emerging as the fourth industry in recent years.

In computer vision and image processing, we study OpenCV and Deep Learning framework such as Tensorflow, Pytorch, and Keras. In research, we study image related deep learning such as image classification, object detection, and image segmentation. If you study computer vision, you can apply for computer vision and deep learning companies, and many companies will want you in the same way as graphics which is the fourth industry field.


Our focus.

We are conducting research in this field.
Computer Graphics

Computer-generated image data created with the help of specialized graphical hardware and software.

Computer Vision

Interdisciplinary scientific field that deals with how computers can be made to gain high-level understanding from digital images or videos.

Deep Learning

A class of machine learning algorithms that use multiple layers to progressively extract higher level features from raw input.

02 - 1

Computer Graphics

  • The representation and manipulation of image data by a computer
  • The various technologies used to create and manipulate images
  • Methods for digitally synthesizing and manipulating visual content
Pixel Art
2D Image
3D Image
Computer Animation
02 - 2

Computer Vision

  • Typical Tasks​


The classical problem in computer vision, image processing, and machine vision is that of determining whether or not the image data contains some specific object, feature, or activity.


Motion analysis

Several tasks relate to motion estimation where an image sequence is processed to produce an estimate of the velocity either at each points in the image or in the 3D scene, or even of the camera that produces the images.


Scene reconstruction

Given one or more images of a scene, or a video, scene reconstruction aims at computing a 3D model of the scene.


Image restoration

The aim of image restoration is the removal of noise (sensor noise, motion blur, etc.) from images.

02 - 3

Deep Learning

Deep learning architectures such as deep neural networks, deep belief networks, recurrent neural networks and convolutional neural networks have been applied to fields including computer vision and so on. 

We mainly use deep learning associated with computer vision.


Contact us

154-42 Gwangkyosan-ro Yeongtong-gu Suwon-si Gyeonggi-do 16227 Republic of Korea.

Room 8501


Professor : Junchul Chun

email : jcchun@kgu.ac.kr
Tel : +82-31-249-9668
Fax : +82-31-253-1165