• The KIPS Transactions:PartB
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• v.9B no.3
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• pp.337-342
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• 2002

This paper proposes a new adaptive image labeling algorithm fur object analysis of the binary images. The proposed labeling algorithm need not merge/order of complex equivalent labels like classical labeling algorithm and the processing is done during only 1 Pass. In addition, this algorithm can be extended for gray-level image easily. Experiment result with HIPR image library shows that the proposed algorithm process more than 2 times laster than compared algorithm.

• Journal of Institute of Control, Robotics and Systems
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• v.12 no.11
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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.

• Journal of IKEEE
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• v.18 no.2
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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.

• Journal of Distribution Science
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• v.18 no.10
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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.

• Proceedings of the KIEE Conference
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• 2000.07d
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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.

• Journal of the Korean Society of Radiology
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• v.9 no.4
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• pp.213-217
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• 2015

To report problems found in a patient who has implemented stent implantation and then conducted a perfusion MRI using ASL(Arterial Spin Labeling), in order to suggest a solution to them. The perfusion MRI was conducted, using pCASL among ASL methods. Data from pCASL(Pseudo Continuous Arterial Spin Labeling) was acquired together with the structural image simply by changing position(labeling gap 15 mm, 170 mm) of the labeling pulse to avoid stent. Data was processed through the ASLtbx. When perfusion MRI was acquired using pCASL, it showed that the position of the conventional labeling pulse (labeling gap 24 mm) was overlapped with that of stent, which made signal intensity in right brain tissue appear as if it were void. When the labeling pulse was positioned (labeling gap 15 mm) to avoid stent, high signal intensity images were acquired. In labeling pulse (labeling gap 170 mm), the signal intensity was more reduced due to relaxation before labeled blood arrived at the imaging slice. pCASL can be stably repeated measurements because it does not use a contrast agent. And it should be selected with the appropriate image acquisition parameters for the high quality image.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.8 no.8
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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.

• Journal of the Korean Society for Precision Engineering
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• v.10 no.4
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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.

• Journal of the Korea Society of Computer and Information
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• v.27 no.9
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• pp.21-32
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• 2022

Image labeling must be preceded in order to perform object detection, and this task is considered a significant burden in building a deep learning model. Tens of thousands of images need to be trained for building a deep learning model, and human labelers have many limitations in labeling these images manually. In order to overcome these difficulties, this study proposes a method to perform object detection without significant performance degradation, even though labeling some images rather than the entire image. Specifically, in this study, low-resolution oriental painting images are converted into high-quality images using a super-resolution algorithm, and the effect of SSIM and PSNR derived in this process on the mAP of object detection is analyzed. We expect that the results of this study can contribute significantly to constructing deep learning models such as image classification, object detection, and image segmentation that require efficient image labeling.

• Journal of information and communication convergence engineering
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• v.9 no.6
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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.