• Proceedings of the Korean Society of Computer Information Conference
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• 2017.01a
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• pp.37-38
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• 2017

이미지 합성은 전경과 배경이 조화롭게 나타나도록 표현하는 것이 필수적이다. 이미지 합성의 품질을 나타내는 현실성이 결여될 경우 객체와 배경이 조화롭게 합성되지 못해 뒤틀리거나 돌출되는 문제가 발생한다. 본 논문에서는 현실성 높은 합성을 위해 이미지 합성 기법들 중에서 현실성을 향상시키는 연구 동향을 조사한다. 이미지 합성 기법 분류에 따라 대표적인 기법을 선택하여 현실성 향상에 대한 연구를 중심으로 소개하고 발전방향을 제시한다.

• Journal of the Korea Convergence Society
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• v.8 no.8
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• pp.33-40
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• 2017

In this paper, we propose a text extraction method by pattern analysis on contour for effective text detection in image. Text extraction algorithms using edge based methods show good performance in images with simple backgrounds, The images of complex background has a poor performance shortcomings. The proposed method simplifies the color of the image by using K-means clustering in the preprocessing process to detect the character region in the image. Enhance the boundaries of the object through the High pass filter to improve the inaccuracy of the boundary of the object in the color simplification process. Then, by using the difference between the expansion and erosion of the morphology technique, the edges of the object is detected, and the character candidate region is discriminated by analyzing the pattern of the contour portion of the acquired region to remove the unnecessary region (picture, background). As a final result, we have shown that the characters included in the candidate character region are extracted by removing unnecessary regions.

• Journal of the Institute of Convergence Signal Processing
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• v.5 no.4
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• pp.249-255
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• 2004

In fingerprint verification and identification, fingerprint and background region should be segmented. For this purpose, most systems obtain variance of brightness of X and Y direction using Sobel mask. To decide given local region is background or not, the variance is compared with a certain threshold. Although this method is simple, most fingerprint image does not separated with two region of fingerprint and background region. In this paper, we presented a new segmentation algorithm based on Run-Length Connectivity analysis. For a given binary image after thresholding, suggested algorithm calculates RL of X and Y direction. Until the given image is segmented to two regions, small run region is successively inverted. Experimental result show that this algorithm effectively separates fingerprint region and background region.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.40 no.1
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• pp.23-30
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• 2022

Recently, there are various studies underway on the deep learning-used image matting methods. Even in the field of photogrammetry, a process of extracting information about relics from images photographed is essential to produce a high-quality realistic model. Such a process requires a great deal of time and manpower, so chroma-key has been used for extraction so far. This method is low in accuracy of sub-classification, however, it is difficult to apply the existing method to high-quality realistic models. Thus, this study attempted to remove background information from high-resolution relic images by using prior background information and trained learning data and evaluate both qualitative and quantitative results of the relic images extracted. As a result, this proposed method with FBA(manual trimap) showed quantitatively better results, and even in the qualitative evaluation, it was high in accuracy of classification around relics. Accordingly, this study confirmed the applicability of the proposed method in the indoor relic photography since it showed high accuracy and fast processing speed by acquiring prior background information when classifying high-resolution relic images.

• Annual Conference of KIPS
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• 2003.05a
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• pp.563-566
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• 2003

인터넷 시대에 접어들면서 웹 카메라를 이용한 보안 시스템의 개발이 활발하다 원격지에 설치된 카메라가 보내준 영상을 통하여 현재의 상황을 파악할 수 있으며 적절한 조치를 웹을 통해 취할 수 있다. 본 논문에서는 카메라로부터 입력되어지는 입력영상과 배경영상의 차를 이용하여 움직임 검출하는 방법을 제안한다. 또한 배경영상은 시간에 따라 변화하기 때문에 변화된 시점부터 배경이미지 픽셀을 교체 해준다. 카메라에서 받아오는 영상을 배경영상과 입력영상으로 구분 한 다음 두 영상의 차를 구하여 영상의 변화점을 찾는다. 픽셀 검사는 모든 픽셀을 연산에 참여하는 방식을 탈피하여 일정한 간격을 두고 이미지의 픽셀을 검색하여 효율적인 객체의 윤곽점을 추출한다.

• Journal of IKEEE
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• v.23 no.4
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• pp.1415-1422
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• 2019

Background subtraction is the major task of computer vision and image processing to detect changes in video. The best performing background subtraction is computationally expensive that cannot be used in real time in a typical computing environment. The proposed algorithm improves the background subtraction algorithm of the widely used MOG with the image resizing algorithm. The proposed image resizing algorithm is designed to drastically reduce the amount of computation and to utilize local information, which is robust against noise such as camera movement. Experimental results of the proposed algorithm have a classification capability that is close to the state of the art background subtraction method and the processing speed is more than 10 times faster.

• Journal of Korea Multimedia Society
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• v.12 no.10
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• pp.1418-1426
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• 2009

There has been a lot of interest in an effective method for background subtraction in an effort to separate foreground objects from a predefined background image. Promising results on background subtraction using statistical methods have recently been reported are robust enough to operate in dynamic environments, but generally require very large computational resources and still have difficulty in obtaining clear segmentation of objects. We use a simple running-average method to model a gradually changing background, instead of using a complicated statistical technique. We employ a single global threshold vector, optimized by a genetic algorithm, instead of pixel-by-pixel thresholds. A new fitness function is defined and trained to evaluate segmentation result. The system has been implemented on a PC with a webcam, and experimental results on real images show that the new method outperforms an existing method based on a mixture of Gaussian.

• Journal of the Korea Computer Graphics Society
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• v.20 no.3
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• pp.1-8
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• 2014

We propose a method to compose a foreground object into a background image, where the foreground object is a part (or a region) of an image taken by a front-facing camera and the background image is a whole image taken by a back-facing camera in a smart phone at the same time. Recent high-end cell-phones have two cameras and provide users with preview video before taking photos. We extract the foreground object that is moving along with the front-facing camera using the optical flow during the preview. We compose the extracted foreground object into a background image using a simple image composition technique. For better-looking result in the composed image, we apply a border smoothing technique using a weighted-border mask to blend transparency from background to foreground. Since constructing and grouping pixel-level dense optical flow are quite slow even in high-end cell-phones, we compute a mask to extract the foreground object in low-resolution image, which reduces the computational cost greatly. Experimental result shows the effectiveness of our extraction and composition techniques, with much less computational time in extracting the foreground object and better composition quality compared with Poisson image editing technique which is widely used in image composition. The proposed method can improve limitedly the color bleeding artifacts observed in Poisson image editing using weighted-border blending.

• Proceedings of the Korean Society of Computer Information Conference
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• 2016.01a
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• pp.227-228
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• 2016

본 연구에서는 게임 배경과 게임 캐릭터의 효과에 대해서 나타낸다. 먼저 3개의 캐릭터 이미지를 주 캐릭터 로 만들 이미지에 합성기법을 적용하여 캐릭터들을 합성한다. 합성하여 만든 캐릭터와 다른 캐릭터의 모양과 형태에 맞게 알맞은 배경 색상으로 만들고, 각 캐릭터의 크기, 원근, 모양, 색상에 따라 효과를 적용한다. 따라서 게임 콘텐츠를 제작하는데 정지 영상으로 표현하여 캐릭터 애니메이션과 장면에 맞는 사운드를 삽입하여 게임 콘텐츠로서 시각과 청각과 움직임이 있는 게임 캐릭터를 제작한다. 특히 배경색, 캐릭터의 번개 및 라이팅 효과색상 같은 것에 관심을 갖고 제작하여 앞으로 게임 콘텐츠의 배경과 캐릭터 효과를 제작하는데 모션그래픽을 이용하여 간단하게 제작 할 수 있음을 나타내었다.

• Database Research
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• v.34 no.3
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• pp.148-158
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• 2018

The optical character recognition (OCR) is a technique to extract and recognize texts from images. It is an important preprocessing step in data analysis since most actual text information is embedded in images. Many OCR engines have high recognition accuracy for images where texts are clearly separable from background, such as white background and black lettering. However, they have low recognition accuracy for images where texts are not easily separable from complex background. To improve this low accuracy problem with complex images, it is necessary to transform the input image to make texts more noticeable. In this paper, we propose a method to segment an input image into text lines to enable OCR engines to recognize each line more efficiently, and to determine the final output by comparing the recognition rates of CLAHE module and Two-step module which distinguish texts from background regions based on image processing techniques. Through thorough experiments comparing with well-known OCR engines, Tesseract and Abbyy, we show that our proposed method have the best recognition accuracy with complex background images.