• 정보처리학회논문지B
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• 제9B권3호
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• pp.337-342
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• 2002

본 논문에서는 이진화 영상의 물체 분석에서 자주 사용되는 새로운 Labeling 알고리즘을 제안한다. 제안한 Labeling 알고리즘은 기존의 Labeling 과는 달리 복잡한 Equivalent Labeling Merging/Ordering이 필요하지 않으며 비재귀적인 Flood-filling에 의하여 1 pass에 Labeling이 이루어진다. 또한 Gray-level 이미지에 대해서도 쉽게 확장될 수 있으며, HIPR Image Library를 대상으로 실험한 결과 기존의 방법보다 2배 이상의 빠른 수행 속도를 보였다.

• 제어로봇시스템학회논문지
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• 제12권11호
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• pp.1156-1161
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• 2006

This paper describes a fast image labeling algorithm for the feature extraction of connected components. Labeling the connected regions of a digitized image is a fundamental computation in image analysis and machine vision, with a large number of application that can be found in various literature. This algorithm is designed for the surface defect inspection of Cold Mill Strip. The labeling algorithm permits to separate all of the connected components appearing on the Cold Mill Strip.

• 전기전자학회논문지
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• 제18권2호
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• pp.234-239
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• 2014

This paper proposes a new automatic detection method to inspect mura defects on display film surface using morphological image processing and labeling. This automatic detection method for mura defects on display films comprises 3 phases of preprocessing with morphological image processing, Gabor filtering, and labeling. Since distorted results could be obtained with the presence of non-uniform illumination, preprocessing step reduces illumination components using morphological image processing. In Gabor filtering, mura images are created with binary coded mura components using Gabor filters. Subsequently, labeling is a final phase of finding the mura defect area using the difference between large mura defects and values in the periphery. To evaluate the accuracy of the proposed detection method, detection rate was assessed by applying the method in 200 display film samples. As a result, the detection rate was high at about 95.5%. Moreover, the study was able to acquire reliable results using the Semu index for luminance mura in image quality inspection.

• 유통과학연구
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• 제18권10호
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• pp.111-119
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• 2020

Purpose: The purpose of the current study is to examine the effects of labeling information on the consumers' evaluation, with a focus on the effects of the three types of labeling information on the product quality. Research design, data and methodology: This study conducted a survey of the women respondents living in Gyeonggi province, Korea, during the time period of April 20th through May 30th, 2020. The sample data have been used to run regression analysis, reliability analysis, frequency analysis and factor analysis. Results: The empirical results are summarized as follows: 1) the labeling information on the brand image has a significantly positive effect on the consumers' evaluation about product's functional quality; 2) the labeling information on the product characteristics has a significantly positive effect on the consumers' evaluation about the expressed quality; and 3) the labeling information on the brand image has a significantly positive effect on the consumers' evaluation about the perceived quality. Conclusions: The conclusion is that the labeling information on product characteristics and the brand image is estimated to be statistically significant, therefore the Korean outdoor-wear industry are required to upgrade the information on the brand image and the product characteristics.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2000년도 하계학술대회 논문집 D
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• pp.3056-3059
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• 2000

This paper describes a fast image labeling algorithm for the feature extraction of connected components. Labeling the connected regions of a digitized image is a fundamental computation in image analysis and machine vision, with a large number of application that can be found in various literature. This algorithm is designed for the surface defect inspection of Cold Mill Strip. The labeling algorithm permits to separate all of the connected components appearing on the Cold Mill Strip.

• 한국방사선학회논문지
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• 제9권4호
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• pp.213-217
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• 2015

스텐트 삽입술을 시행한 환자에게 ASL 방법 중 pCASL을 이용한 관류영상에서 나타난 인공물을 보고하고 이에 대한 해결방법을 제시하고자 한다. pCASL데이터는 구조적 이미지와 함께 스텐트를 피해 표지 펄스(labeling pulse)의 위치를 변경하여 획득하였다. 데이터는 ASLtbx를 이용하여 처리하였다. pCASL을 이용하여 관류영상을 획득하였을 때 기존의 표지 펄스(표지 간격(labeling gap) 24 mm)의 위치가 스텐트의 위치와 겹쳐져서 우뇌 조직의 신호강도가 비어 있는 것처럼 나타났다. 스텐트를 피해 표지 펄스(표지 간격 15 mm)를 위치시킬 때 높은 신호강도의 영상을 획득할 수 있었으며, 표지 펄스(표지 간격 170 mm)에서는 labeled 혈액이 영상절편에 도달하기 전에 이완이 되어 낮은 신호강도의 영상을 획득 하였다. pCASL은 조영제를 사용하지 않기 때문에 안정적으로 반복측정이 가능하며 양질의 영상 획득을 위해서는 알맞은 영상획득인자와 방법들이 선택되어야 한다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제8권8호
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• pp.2881-2894
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• 2014

In complicated environment, context information plays an important role in image segmentation/labeling. The recently proposed auto-context algorithm is one of the effective context-based methods. However, the standard auto-context approach samples the context locations utilizing a fixed radius sequence, which is sensitive to large scale-change of objects. In this paper, we present a scale invariant auto-context (SIAC) algorithm which is an improved version of the auto-context algorithm. In order to achieve scale-invariance, we try to approximate the optimal scale for the image in an iterative way and adopt the corresponding optimal radius sequence for context location sampling, both in training and testing. In each iteration of the proposed SIAC algorithm, we use the current classification map to estimate the image scale, and the corresponding radius sequence is then used for choosing context locations. The algorithm iteratively updates the classification maps, as well as the image scales, until convergence. We demonstrate the SIAC algorithm on several image segmentation/labeling tasks. The results demonstrate improvement over the standard auto-context algorithm when large scale-change of objects exists.

• 한국정밀공학회지
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• 제10권4호
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• pp.248-254
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• 1993

This paper proposes the labeling method for solving bin-picking problem in robot vision. It has the processing steps such as image thresholding, region labeling, and moment computation. To determine a target object from object, the modified labeling method is used to. The moment concept applied to determine the position and orientation of target object. Finally, some experiment result are illustrated and compared with the results of conventional shrinking algorithm and collision fronts algorithm. The proposed labeling method has reduced processing time.

• 한국컴퓨터정보학회논문지
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• 제27권9호
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• pp.21-32
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• 2022

이미지에 레이블을 부착하는 레이블링은 객체 탐지를 수행하기 위해서는 반드시 선행되어야 하며 이러한 작업은 딥러닝 모델을 구축하는 데 있어서 큰 부담으로 여겨지고 있다. 딥러닝 모델을 훈련하기 위해서는 수 만장의 이미지가 필요하며 이러한 이미지에 인간 레이블러가 직접 레이블링을 진행하기에는 많은 한계가 있다. 이러한 어려움을 극복하기 위해 본 연구에서는 전체 이미지가 아닌 일부 이미지에 대한 레이블링을 통해서도 큰 성능의 저하 없이 객체 탐지를 수행하는 방안을 제안한다. 구체적으로 본 연구에서는 저품질 동양화 이미지의 객체 탐지를 위해 초고해상화 알고리즘을 이용하여 저해상도의 이미지를 고화질의 이미지로 변환하고, 이 과정에서 도출되는 SSIM과 PSNR이 객체 탐지의 mAP에 미치는 영향을 분석하여 객체 탐지 분석에 필요한 레이블링을 위한 최적의 샘플링을 수행하는 방안을 제안한다. 본 연구의 결과는 이미지 레이블링을 필요로 하는 이미지 분류, 객체 검출, 이미지 분할 등 딥러닝 모델 구축에 크게 기여할 수 있을 것으로 기대한다.

• Journal of information and communication convergence engineering
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• 제9권6호
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• pp.661-665
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• 2011

This paper presents the method for the extraction of the lungs part from the other parts for the diagnostic of the lungs part. The proposed method is based on the calculation of the connected component and the centroid of the image. Connected Component labeling is used to label the each objects in the binarized image. After the labeling is done, centroid value is calculated for each object. The filing operation is applied which helps to extract the lungs part from the image retaining all the parts of the original lungs image. The whole process is explained in the following steps and experimental results shows it's significant.