• Journal of the Korean institute of surface engineering
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• v.54 no.5
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• pp.278-284
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• 2021

Corrosion of steel plate is common phenomenon which results in the gradual destruction caused by a wide variety of environments. Corrosion monitoring is the tracking of the degradation progress for a long period of time. Corrosion on steel plate appears as a discoloration and any irregularities on the surface. In this study, we developed a quantitative evaluation method of the rust formed on steel plate by using k-means clustering from the corroded area in a given image. The k-means clustering for automated corrosion detection was based on the GrabCut segmentation and Gaussian mixture model(GMM). Image color of the corroded surface at cut-edge area was analyzed quantitatively based on HSV(Hue, Saturation, Value) color space.

• The Journal of the Korea Contents Association
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• v.13 no.6
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• pp.1-8
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• 2013

It is generally required to use modeling or animation tools for inserting dynamic objects into augmented reality, and this process demands high expertise and complexity. This paper proposes a video object based authoring method that enables augmentation with dynamic video objects without such process. Integrated grab-cut and grow-cut method strips initial area of video target off the existing video clips, and snap-cut method is then applied to track objects' boundaries over frames so as to augment real world with continuous motion frames. Experiment shows video cut-out and authoring results achieved by only a few menu selections and simple correcting sketch.

• Journal of the Korea Society of Computer and Information
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• v.22 no.12
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• pp.63-68
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• 2017

In this paper, we propose a bounding box prediction algorithm using multiple probability maps to improve object detection result of object detector. Although the performance of object detectors has been significantly improved, it is still not perfect due to technical problems and lack of learning data. Therefore, we use the result correction method to obtain more accurate object detection results. In the proposed algorithm, the preprocessed bounding box created as a result of object detection by the object detector is clustered in various form, and a conditional probability is given to each cluster to make multiple probability map. Finally, multiple probability map create new bounding box of object using morphological elements. Experiment results show that the newly predicted bounding box reduces the error in ground truth more than 45% on average compared to the previous bounding box.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.22 no.4
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• pp.617-623
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• 2018

In this paper, we propose a novel seamless video composition method based on shape matching and Poisson equation. Video composition method consists of video segmentation process and video blending process. In the video segmentation process, the user first sets a trimap for the first frame, and then performs a grab-cut algorithm. Next, considering that the performance of video segmentation may be reduced if the color, brightness and texture of the object and the background are similar, the object region segmented in the current frame is corrected through shape matching between the objects of the current frame and the previous frame. In the video blending process, the object of source video and the background of target video are blended seamlessly using Poisson equation, and the object is located according to the movement path set by the user. Simulation results show that the proposed method has better performance not only in the naturalness of the composite video but also in computational time.

• Journal of Broadcast Engineering
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• v.26 no.2
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• pp.175-183
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• 2021

The study of the fine grained classification of images continues to develop, but the study of object recognition for animals with polymorphic properties is proceeding slowly. Using only pet images corresponding to dogs and cats, this paper aims to compare methods using image processing and methods using deep learning among methods of classifying species of animals, which are fine grained classifications. In this paper, Grab-cut algorithm is used for object segmentation by method using image processing, and method using Fisher Vector for image encoding is proposed. Other methods used deep learning, which has achieved good results in various fields through machine learning, and among them, Convolutional Neural Network (CNN), which showed outstanding performance in image recognition, and Tensorflow, an open-source-based deep learning framework provided by Google. For each method proposed, 37 kinds of pet images, a total of 7,390 pages, were tested to verify and compare their effects.

• Journal of International Society for Simulation Surgery
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• v.4 no.1
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• pp.21-23
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• 2017

Purpose In this paper, we proposed alternate neighborhood system and reverse spread-direction approach for accurate and fast cellular automata-based image segmentation method. Materials and Methods On the basis of a simple but effective interactive image segmentation technique based on a cellular automaton, we propose an efficient algorithm by using Moore and designed neighborhood system alternately and reversing the direction of the reference pixels for spreading out to the surrounding pixels. Results In our experiments, the GrabCut database were used for evaluation. According to our experimental results, the proposed method allows cellular automata-based image segmentation method to faster while maintaining the segmentation quality. Conclusion Our results proved that proposed method improved accuracy and reduced computation time, and also could be applied to a large range of applications.

• Journal of Korea Multimedia Society
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• v.17 no.9
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• pp.1125-1133
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• 2014

Recently, there are many researches have been studying for analyzing user interests and emotions based on users profiles and diverse information from Social Network Services (SNSs) due to their popularities. However, most of traditional researches are focusing on their researches based on single resource such as text, image, hash tag, and more, in order to obtain what user emotions are. Hence, this paper propose a method for obtaining user emotional information by analyzing texts and images both from Instagram which is one of the well-known image based SNSs. In order to extract emotional information from given images, we firstly apply GRAB-CUT algorithm to retrieve objects from given images. These retrieved objects will be regenerated by their representative colors, and compared with emotional vocabulary table for extracting which vocabularies are the most appropriate for the given images. Afterward, we will extract emotional vocabularies from text information in the comments for the given images, based on frequencies of adjective words. Finally, we will measure WUP similarities between adjective words and emotional words which extracted from the previous step. We believe that it is possible to obtain more precise user emotional information if we analyzed images and texts both time.

• Journal of Multimedia Information System
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• v.7 no.2
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• pp.97-106
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• 2020

The subjective experience would degrade the current artificial artistic aesthetic analysis. Since Qiong kiln ceramics have a long history and occupy a very important position in ceramic arts, we employed computer-aided technologies to quickly automatically accurately and quantitatively process a large number of Qiong kiln ceramic images and generate the detailed statistical data. Because the color features are simple and significant visual characteristics, the color features of Qiong kiln ceramics are analyzed for the quantitative aesthetics. The Qiong kiln ceramic images are segmented with GrabCut algorithm. Three moments (1st-order, 2nd-order, and 3rd-order) are calculated in two typical color spaces, namely RGB and HSV. The discrimination powers of the color features are analyzed according to various dynasties (Tang Dynasty, Five Dynasties, Song Dynasty) and various utensils (Pot, kettle, bowl), which are helpful to the selection of the discriminant color features among various dynasties and utensils. This paper is helpful to promoting the quantitative aesthetic research of Qiong kiln ceramics and is also conducive to the research on the aesthetics of other ceramics.

• Proceedings of the Korea Information Processing Society Conference
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• 2023.05a
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• pp.8-10
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• 2023

삼킴장애는 음식물이 입에서 식도로 가지않고 걸리거나 기도(Trachea)로 흡입되는 문제를 갖는 상태이다. 특히 노인이나 신경계 질환을 앓는 환자의 경우 기도로 흡입된 음식덩이가 폐렴을 일으키고 결국에는 사망으로 이어지기에 적절한 치료와 관리가 요구된다. 보통 영상으로 판단할 수 있는 삼킴단계는 구강준비단계(Oral Preparatory Phase), 구강단계(Oral Phase), 인두단계(Pharyngeal Phase), 식도단계(Esophageal Phase) 4가지로 분류하고 삼킴장애는 침습(Penetration)과 흡인(Aspiration)으로 크게 2가지로 분류한다. 본 논문에서는 이러한 6가지 클래스를 가지는 삼킴장애 환자 비디오 파일을 라벨링하기 위한 웹 애플리케이션을 제안한다. 이를 구현하기 위해서 대용량 멀티프레임 이미지를 수신해서 분리하여 저장하도록 개발하였다. 또한 음식덩이를 정교하게 분할할 수 있도록 GrabCut 알고리즘을 적용하여 라벨링할 수 있도록 하였다. 차후 라벨러와 전문의 간의 협업이 가능하도록 라벨링 데이터의 상태를 관리할 수 있도록 개발하고자 한다.

• Journal of Korea Multimedia Society
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• v.17 no.3
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• pp.300-311
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• 2014

According to fast change of the environment, the structured study of the ecosystem by analyzing the plant leaves are needed. Expecially, the methodology that searches and classifies the leaves from captured from the smart device have received numerous concerns in the field of computer science and ecology. In this paper, we propose a plant leaf classification technique using shape descriptor by combining Scale Invarinat Feature Transform (SIFT) and Histogram of Oriented Gradient (HOG) from the image segmented from the background via Graphcut algorithm. The shape descriptor is coded in the field of Locality-constrained Linear Coding to optimize the meaningful features from a high degree of freedom. It is connected to Support Vector Machines (SVM) for efficient classification. The experimental results show that our proposed approach is very efficient to classify the leaves which have similar color, and shape.