• Journal of KIISE
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• v.43 no.3
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• pp.346-352
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• 2016

The fishing industry requires many workers to manually carry out the jobs of sorting and cutting fishes. There are therefore many dangerous situations in their working environment and the throughput is inefficiently low. This paper introduces an automatic fin cutting system based on RANSAC that is able to increase the throughput of fish processing jobs. The system proposed in this paper first detects the edges of a fish using a high-pass filter. The boundary lines between fin and body are then detected by adjusting parameters and the threshold of the noise filters. Finally, the optimal cutting lines are detected using RANSAC. Through an experiment with a sample of 50 fishes, this paper shows that the proposed system detects the cutting lines with about 90% accuracy.

• Journal of Advanced Navigation Technology
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• v.24 no.2
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• pp.148-154
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• 2020

Wrinkles are one of the main features of skin aging. Conventional image processing-based wrinkle detection is difficult to effectively cope with various skin images. In particular, Wrinkle extraction performance is significantly decreased when the wrinkles are not strong and similar to the surrounding skin. In this paper, deep learning is applied to extract wrinkles from microscopic skin images. In general, the microscope image is equipped with a wide-angle lens, so the brightness at the boundary area of the image is dark. In this paper, to solve this problem, the brightness of the skin image is estimated and corrected. In addition, We apply the structure of semantic segmentation network suitable for wrinkle extraction. The proposed method obtained an accuracy of 99.6% in test experiments on skin images collected in our laboratory.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 1998.06b
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• pp.92-97
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• 1998

For the efficient searching and browsing of digital video, it is essential to extract the internal structure of the video contents. As an example, a news video consists of several sections such as politics, economics, sports and others, and also each section consists of individual topics. With this information in hand, users can ore easily access the required video frames. This paper addresses the problem of automatic shot boundary detection and selection of representative frames (R-frames), which are the essential step in recognizing the internal structure of video contents. In the shot boundary detection, a new algorithm that have dual detectors which are designed specifically for the abrupt boundaries (cuts) and gradually changing bounaries respectively is proposed. Compared to the existing 미algorithms that mostly have tried to detect both types by a single mechanism, the proposed algorithm is proved to be more robust and accurate. Also in the problem of R-frame selection, simple mechanical approaches such as selecting one frame every other second have been adopted. However this approach often selects too many R-frames in static short, while drops important frames in dynamic shots. To improve the selection mechanism, a new R-frame selection algorithm that uses motion information extracted from pixel difference is proposed.

• Journal of the Korea Society of Computer and Information
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• v.17 no.12
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• pp.101-109
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• 2012

In many diagnostic and treatment procedures for prostate disease accurate detection of prostate boundaries in transrectal ultrasound(TRUS) images is required. This is a challenging and difficult task due to weak prostate boundaries, speckle noise and the short range of gray levels. In this paper a method for automatic prostate segmentation inTRUS images using support vectors and snake-like contour is presented. This method involves preprocessing, extracting Gabor feature, training, and prostate segmentation. Gabor filter bank for extracting the texture features has been implemented. A support vector machine(SVM) for training step has been used to get each feature of prostate and nonprostate. The boundary of prostate is extracted by the snake-like contour algorithm. The results showed that this new algorithm extracted the prostate boundary with less than 9.3% relative to boundary provided manually by experts.

• 한국지형공간정보학회:학술대회논문집
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• 2002.11a
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• pp.117-122
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• 2002

Change detection is a useful technology that can be applied to various fields, taking temporal change information with the comparison and analysis among multi-temporal satellite images. Especially, change detection that utilizes high-resolution satellite imagery can be implemented to extract useful change information for many purposes, such as the environmental inspection, the circumstantial analysis of disaster damage, the inspection of illegal building, and the military use, which cannot be achieved by lower middle-resolution satellite imagery. However, because of the special characteristics that result from high-resolution satellite imagery, it cannot use a pixel-based method that is used for low-resolution satellite imagery. Therefore, it must be used a feature-based algorithm based on the geographical and morphological feature. This paper presents the system that builds the change map by digitizing the boundary of the changed object. In this system, we can make the change map using manual or semi-automatic digitizing through the user interface implemented with a floating window that enables to detect the sign of the change, such as the construction or dismantlement, more efficiently.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2001.10a
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• pp.363-366
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• 2001

The present process of grading leather quality by the rare eyes is not reliable. Because inconsistency of grading due to eyes strain for long time can cause incorrect result of grading. Therefore it is necessary to automate the process of grading quality of leather based on objective standard for it. In this paper, leather automatic classification system consists of the process obtaining the information of leather and the process grading the quality of leather from the information. Leather is graded by its information such as texture density, types and distribution of defects. This paper proposes the algorithm which sorts out leather information like texture density and defects from the gray-level images obtained by digital camera. The density information is sorted out by the distribution value of Fourier spectrum which comes out after original image is converted to the image in frequency domain. And the defect information is obtained by the statistics of pixels which is relevant to Window using searching Window after sort out boundary lines from preprocessed images. The information for entire leather is used as standard of grading leather quality, and the proposed algorithm is practically applied to machine vision system.

• 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.

• The Korean Journal of Nuclear Medicine
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• v.32 no.5
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• pp.414-424
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• 1998

Purpose: Cross-modality coregistration of positron emission tomography (PET) and magnetic resonance imaging (MR) could enhance the clinical information. In this study we propose a refined technique to improve the robustness of registration, and to implement more realistic visualization of the coregistered images. Materials and Methods: Using the sinogram of PET emission scan, we extracted the robust head boundary and used boundary-enhanced PET to coregister PET with MR. The pixels having 10% of maximum pixel value were considered as the boundary of sinogram. Boundary pixel values were exchanged with maximum value of sinogram. One hundred eighty boundary points were extracted at intervals of about 2 degree using simple threshold method from each slice of MR images. Best affined transformation between the two point sets was performed using least square fitting which should minimize the sum of Euclidean distance between the point sets. We reduced calculation time using pre-defined distance map. Finally we developed an automatic coregistration program using this boundary detection and surface matching technique. We designed a new weighted normalization technique to display the coregistered PET and MR images simultaneously. Results: Using our newly developed method, robust extraction of head boundary was possible and spatial registration was successfully performed. Mean displacement error was less than 2.0 mm. In visualization of coregistered images using weighted normalization method, structures shown in MR image could be realistically represented. Conclusion: Our refined technique could practically enhance the performance of automated three dimensional coregistration.

• Journal of Environmental Science International
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• v.19 no.1
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• pp.27-37
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• 2010

A system coupled the prognostic WRF mesoscale model and CALMET diagnostic model has been employed for predicting high-resolution wind field over complex coastal area. WRF has three nested grids down to from during two days from 24 August 2007 to 26 August 2007. CALMET simulation is performed using both initial meteorological field from WRF coarsest results and surface boundary condition that is Shuttle Radar Topography Mission (SRTM) 90m topography and Environmental Geographic Information System (EGIS) 30m landuse during same periods above. Four Automatic Weather System (AWS) and a Sonic Detection And Ranging (SODAR) are used to verify modeled wind fields. Horizontal wind fields in CM_100m is not only more complex but better simulated than WRF_1km results at Backwoon and Geumho in which there are shown stagnation, blocking effects and orographically driven winds. Being increased in horizontal grid spacing, CM_100m is well matched with vertically wind profile compared SODAR. This also mentions the importance of high-resolution surface boundary conditions when horizontal grid spacing is increased to produce detailed wind fields over complex terrain features.

• Proceedings of the KIEE Conference
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• 2008.10b
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• pp.141-142
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• 2008

본 논문은 텍스처된 3차원 점군데이터를 효율적으로 모델링하는 방법을 제안한다. 지상라이다로부터 획득한 3차원 점군데이터는 많은 노이즈를 가지고 있으며 이로 인해 자동적인 모델링이 어렵다. 3차원 모델링에 있어서 메쉬를 생성해야 3차원 랜더링이 가능하지만 3차원 메쉬 생성은 노이즈에 취약하기 때문에 디자이너들이 수작업으로 노이즈를 제거해야만 한다. 하지만 노이즈 자제가 지상 라이다로부터 들어온 데이터이기 때문에 자동적인 노이즈 제거가 어렵다. 본 논문에서는 텍스처된 지상 라이다 데이터로부터 칼라 영상의 정보를 이용한 윤곽선 정보 검출 방법을 제안한다. 대부분의 건물과 같은 구조물에서 최 외곽은 같은 색의 정보를 가지고 있다. 최 외곽 칼라의 정보를 이용하여 칼라 정보의 변화를 제한하고, 유사 칼라 정보를 가지고 있는 픽셀만 얻어냄으로써 최외각 정보를 얻어낸다. 칼라 이미지를 이용만 필터링 된 점군데이터는 xy, xz, yz 각각의 평면에서 윤곽선 데이터를 가지며 이는 구조물에 대한 모델링의 속도를 빠르게 해준다.