• Journal of the Institute of Electronics Engineers of Korea SP
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• v.40 no.1
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• pp.51-62
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• 2003

A new video segmentation algorithm for segmentation-based video coding is proposed. The method uses a new criterion based on similarities in both motion and brightness. Brightness and motion information are incorporated in a single segmentation procedure. The actual segmentation is accomplished using a region-growing technique based on the watershed algorithm. In addition, a tracking technique is used in subsequent frames to achieve a coherent segmentation through time. Simulation results show that the proposed method is effective in determining object boundaries not easily found using the statistic criterion alone.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.4 no.3
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• pp.126-133
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• 2003

This paper is related to a morphological segmentation method for extracting the moving object in video sequence using global motion compensation and two-dimensional spatio-temporal entropic thresholding. First, global motion compensation is performed with camera panning vector estimated in the hierarchical pyramid structure constructed by wavelet transform. Secondly, the regions with high possibility to include the moving object between two consecutive frames are extracted block by block from the global motion compensated image using two-dimensional spatio-temporal entropic thresholding. Afterwards, the LUT classifying each block into one among changed block, uncertain block, stationary block according to the results classified by two-dimensional spatio-temporal entropic thresholding is made out. Next, by adaptively selecting the initial search layer and the search range referring to the LUT, the proposed HBMA can effectively carry out fast motion estimation and extract object-included region in the hierarchical pyramid structure. Finally, after we define the thresholded gradient image in the object-included region, and apply the morphological segmentation method to the object-included region pixel by pixel and extract the moving object included in video sequence. As shown in the results of computer simulation, the proposed method provides relatively good segmentation results for moving object and specially comes up with reasonable segmentation results in the edge areas with lower contrast.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.41 no.6
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• pp.25-32
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• 2004

We propose a hierarchical disparity estimation algorithm with edge-preserving energy-based regularization. Initial disparity vectors are obtained from downsampled stereo images using a feature-based region-dividing disparity estimation technique. Dense disparities are estimated from these initial vectors with shape-adaptive windows in full resolution images. Finally, the vector fields are regularized with the minimization of the energy functional which considers both fidelity and smoothness of the fields. The first two steps provide highly reliable disparity vectors, so that local minimum problem can be avoided in regularization step. The proposed algorithm generates accurate disparity map which is smooth inside objects while preserving its discontinuities in boundaries. Experimental results are presented to illustrate the capabilities of the proposed disparity estimation technique.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.22 no.5
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• pp.747-753
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• 2018

In the license-plate recognition systems for automobile black Image, the license-plate image frequently has a shadow due to outdoor environments which are frequently changing. Such a shadow makes unpredictable errors in the segmentation process of individual characters and numbers of the license plate image, and reduces the overall recognition rate. In this paper, to improve the recognition rate in these circumstance, a license-plate image binarization algorithm is proposed removing the shadow effectively. The propose algorithm splits the license-plate image into the regions with the shadow and without. To find out the boundary of two regions, the algorithm estimates the curve for shadow boundary using the least-squares method. The simulation is performed for the license-plate image having its shadow, and the results show much higher recognition rate than the previous algorithm.

• The KIPS Transactions:PartB
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• v.10B no.3
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• pp.339-346
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• 2003

In this paper, we propose a new method in which interpolation images are created by using a small number of axiai T2-weighted images instead of using many sectional images for 3D visualization of brain MR images. For image Interpolation, an important part of this process, we first segment a region of interest (ROI) that we wish to apply 3D reconstruction and extract the boundaries of segmented ROIs and MBR information. After the image size of interpolation layer is determined according to the changing rate of MBR size between top slice and bottom slice of segmented ROI, we find the corresponding pixels in segmented ROI images. Then we calculate a pixel's intensity of interpolation image by assigning to each pixel intensity weights detected by cube interpolation method. Finally, 3D reconstruction is accomplished by exploiting feature points and 3D voxels in the created interpolation images.

• Journal of the Korean Institute of Telematics and Electronics
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• v.26 no.7
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• pp.132-139
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• 1989

Optical flow is important not only for determining velocity and trajectory of the object but also for image segmentation and 3D information. The gradient-based method is mostly used to compute optical flow form image sequences, but it accomanies smoothing effect of velocity vectors. In this paper, an enhanced algorithm for computing optical flow using scalar edge to restrict is also applied to reduce errors both around motion boundary and in the occlusion region.

• 전자공학회논문지 IE
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• v.43 no.4
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• pp.43-51
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• 2006

Segmentation method using RSST has many advantages in extracting of accurate region boundaries and controlling the resolution of segmented result and so on. In this paper, we propose three fast RSST algorithms for image segmentation. In first method, we classify links according to weight size for fast link search. In the second method, very similar links before RSST construction are eliminated. In third method, the links of very small regions which are not important for human eye are eliminated. As a result, the total times elapsed for segmentation are reduced by about 10 $\sim$ 40 times, and reconstructed images based on the segmentation results show little degradation of PSNR and visual quality.

• Journal of the Microelectronics and Packaging Society
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• v.30 no.4
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• pp.69-78
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• 2023

With the rapid growth of artificial intelligence, the demand for semiconductors is enormously increasing everywhere. To ensure the manufacturing quality and quantity simultaneously, the importance of automatic defect detection during the packaging process has been re-visited by adapting various deep learning-based methodologies into automatic packaging defect inspection. Deep learning (DL) models require a large amount of data for training, but due to the nature of the semiconductor industry where security is important, sharing and labeling of relevant data is challenging, making it difficult for model training. In this study, we propose a new framework for securing sufficient data for DL models with fewer computing resources through a divide-and-conquer approach. The proposed method divides high-resolution images into pre-defined sub-regions and assigns conditional labels to each region, then trains individual sub-regions and boundaries with boundary loss inducing the globally coherent and seamless images. Afterwards, full-size image is reconstructed by combining divided sub-regions. The experimental results show that the images obtained through this research have high efficiency, consistency, quality, and generality.

• Proceedings of the Korea Information Processing Society Conference
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• 2000.04a
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• pp.1077-1083
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• 2000

본 논문은 운송 컨테이너 식별자의 자동화 처리를 위한 문자 인식의 단계중 최종 문자 인식 전단계 까지의 처리 과정을 컨테이너의 특성에 맞게 제안하였으며, 이러한 전처리 과정은 문자 인식 시스템의 성능에 중요한 영향을 미친다. 제안한 방법은 먼저 입력된 컨테이너 컬러 영상을 명암 영상으로 바꾸고 전체 영상중 인식에 필요한 식별자 영역만을 경계선 검출과 형태학적 연산을 이용하여 추출한다. 이어서 다양한 배경색과 문자색을 판단하여 일반 문서와 같이 일관성있게 통일한 후, DCT를 이용한 명암도별 이진영역으로 분할한 후에 Otsu방법과 새로운 이진화방법을 자동으로 선택하여 효율적인 이진화가 이루어지도록 하였다. 이렇게 얻어진 이진 영상은 문자인식 단계로 넘어갈 수 있도록 개별 문자로 분할한다. 이 방법은 컨테이너 영상의 불균등한 배경색과 잡음으로 인하여 문자인식에 오류가 생기는 단점을 보완하였으며 컨테이너 특성을 최대한 반영함으로써 효과적인 전처리 결과를 얻을 수 있었다. 또한, 제안한 방법의 응용은 컨테이너 이외의 다른 상황에서도 매우 효과적으로 사용될 수 있으리라 본다.

• Proceedings of the Korea Information Processing Society Conference
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• 2005.05a
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• pp.1661-1664
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• 2005

본 논문에서는 computer-aided analysis 의 일환으로 X-ray 심혈관 조영도를 이용하여 관상동맥의 구조를 보여주는 방법에 대해 제시하고자 한다. 관상동맥 폐색증 환자들에게 시술되는 스텐트 삽입 시술이나 관상동맥 우회로 시술을 할 때에는 X-ray 의 조영 영상이 매우 중요한 시술의 기준이 되고 있으며, 조영 영상에서 혈관을 빠르고 정확하게 인식하는 것은 시술의 필수 조건이다. 이러한 시술중의 혈관구조 인식을 돕기 위하여 본 논문에서는 심혈관 조영 영상으로부터 관상동맥의 골격을 추출하기 위한 방법을 제안한다. 본 논문에서는 혈관 구조 추출을 위하여 3 단계 알고리즘을 제시한다. 첫번째 단계에서는 조영도에서 잡음을 제거하기 위하여 동질영역을 블러링할 수 있는 speckle reducing anisotropic diffusion 을 이용한 이미지 필터링을 수행한다. 이 필터링은 영상내 잡음을 제거하고 혈관의 경계선을 강화하여 정확한 영상인식을 가능하게 한다. 두번째 단계에서는 영상 내에서 보여지는 주요 혈관을 분할하는 것이다. 이 영상분할에는 canny edge detection 과 개선된 영역확장법(adaptive region growing)을 동시에 이용하는 복합적 분할기법이 수행된다. 세번째 단계에서는 형태학적 기법(Morphology)을 이용하여 분할결과의 부족한 부분을 보완하고 골격화를 수행하여 정확한 혈관 구조를 추출해낸다. 실험을 위해서는 정상인의 관상동맥 영상 뿐 아니라 혈관이 가늘어지는 폐색이 관찰되는 환자의 영상에 대해서도 실험하였다. 또한 논문에서 제시한 알고리즘에 대한 검증을 위하여 실험 결과들은 의료진의 감수를 거쳤다.