• Journal of Information Processing Systems
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• 제11권3호
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• pp.421-437
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• 2015

This paper aims to present a supervised classification algorithm based on data fusion for the segmentation of the textured images. The feature extraction method we used is based on discrete wavelet transform (DWT). In the segmentation stage, the estimated feature vector of each pixel is sent to the support vector machine (SVM) classifier for initial labeling. To obtain a more accurate segmentation result, two strategies based on information fusion were used. We first integrated decision-level fusion strategies by combining decisions made by the SVM classifier within a sliding window. In the second strategy, the fuzzy set theory and rules based on probability theory were used to combine the scores obtained by SVM over a sliding window. Finally, the performance of the proposed segmentation algorithm was demonstrated on a variety of synthetic and real images and showed that the proposed data fusion method improved the classification accuracy compared to applying a SVM classifier. The results revealed that the overall accuracies of SVM classification of textured images is 88%, while our fusion methodology obtained an accuracy of up to 96%, depending on the size of the data base.

• 한국정보과학회논문지:소프트웨어및응용
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• 제27권1호
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• pp.84-95
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• 2000

본 논문에서는 명사구 색인과 복합명사 분할을 포함한 복합명사 처리를 위해 통계 정보와 자연언어 처리를 제한적으로 이용 가능하게 하는 통합적 색인 기법을 제안한다. 먼저 색인과 검색에서 복합명사 분할 및 합성 모두를 고려한 통합 기법을 제시하고, 이를 위해 통계 정보와 제한적인 자연언어 처리를 모두 이용하는 통합 색인 기법을 제안한다. 먼저 형태소 분석 및 태깅 과정에서 단일어를 색인어로 추출하고 구문분석의 결과에서 명사구를 합성해 낸다. 구문 분석 실패 시에는 형태소 분석 및 태깅의 결과만을 사용하게 된다. 또한 태깅의 결과에서 복합명사를 골라 통계 정보를 이용하여 단일 명사로 분할하고 재합성한다. 분할된 단일 명사와 합성된 명사구는 기존의 단일어로만 이루어진 색인어를 보완하기 위해 색인어로 사용된다. 실험은 한국어 정보검색의 실험 집합인 KTSET 2.0과 KRIST SET을 사용하여 통합색인 기법이 복합명사 처리에 효율적임을 보였다.

• Journal of Computing Science and Engineering
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• 제8권2호
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• pp.119-128
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• 2014

The appearance of retinal blood vessels is an important diagnostic indicator of serious disease, such as hypertension, diabetes, cardiovascular disease, and stroke. Automatic segmentation of the retinal vasculature is a primary step towards automatic assessment of the retinal blood vessel features. This paper presents an automated method for the enhancement and segmentation of blood vessels in fundus images. To decrease the influence of the optic disk, and emphasize the vessels for each retinal image, a multidirectional morphological top-hat transform with rotating structuring elements is first applied to the background homogenized retinal image. Then, an improved multiscale line detector is presented to produce a vessel response image, and yield the retinal blood vessel tree for each retinal image. Since different line detectors at varying scales have different line responses in the multiscale detector, the line detectors with longer length produce more vessel responses than the ones with shorter length; the improved multiscale detector combines all the responses at different scales by setting different weights for each scale. The methodology is evaluated on two publicly available databases, DRIVE and STARE. Experimental results demonstrate an excellent performance that approximates the average accuracy of a human observer. Moreover, the method is simple, fast, and robust to noise, so it is suitable for being integrated into a computer-assisted diagnostic system for ophthalmic disorders.

• 한국산업정보학회논문지
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• 제15권5호
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• pp.79-87
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• 2010

본 논문에서는 현재 대학 한방병원에서 운영하고 있는 통합의료정보시스템의 외래환자 인구학적 정보와 진료기록 정보를 이용하여 고객세분화를 실시하고, 그 결과를 활용하여 외래환자 고객만족도 증진을 위한 고객관계관리 시스템 구축 모형을 제안하였다. 제안된 고객 관계관리 시스템 모형은 최선 정보기술과 인프라를 이용하기 보다는 현재 구축된 병원정보시스템의 부분적 수정을 통해 구축이 가능하므로 즉시 실현이 가능한 실용적인 모델이 될 수 있다. 또한 마케팅 전략에 따라 적절한 변수와 세분화 방법을 활용할 경우, 외래환자 고객만족 증진뿐만 아니라 다양한 형태의 마케팅 전략을 지원할 수 있는 고객관계관리 시스템 구축이 가능할 것이다.

• 한국정보전자통신기술학회논문지
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• 제11권5호
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• pp.615-622
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• 2018

최근 가시광원을 사용하는 카메라 통신 기술의 발전과 더불어 디스플레이를 통해 가시광 데이터를 표출하고 이를 인식하는 기술에 대한 수요가 증가하고 있다. 기존의 디스플레이 기반 CamCom 기법은 사용자가 설정한 RoI 영역 기반의 2차원 컬러코드를 인식하는 방식을 사용하였으나, 이는 보행 상황 등 수신위치가 변동되는 상황에 적합하지 않은 단점이 존재한다. 이에 본 논문에서는 카메라 통신에서 자동 RoI 설정을 위해 적용될 수 있는 Lambertian 색상 분할과 Canny 엣지 검출이 결합된 알고리즘 기반의 자동 디스플레이 검출 기법에 대하여 제안하였다. 기존 디스플레이 검출 기법은 디스플레이에서 표출되고 있는 콘텐츠의 변화가 발생하면 검출율이 현저히 감소하는 문제점이 존재하며, 본 논문에서는 이를 해결하기 위하여 lambertian 색상 분할 및 canny 엣지 검출을 결합한 알고리즘 적용을 통헤 자동으로 디스플레이를 검출 할 수 있는 기법을 제안하였다. 본 연구에서는 디스플레이 엣지 인식을 위해 사용되는 다양한 알고리즘을 분석하고 변화하는 컬러코드 콘텐츠 인식시 성능을 측정하였으며, 제안한 저속 카메라 통신용 자동 디스플레이 검출을 위한 lambertian 색상 분할 및 Canny Edge Detection 알고리즘을 적용한 실험 결과 약 96%의 검출율을 달성함을 확인하였다.

• 대한원격탐사학회지
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• 제27권3호
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• pp.379-388
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• 2011

At present, GEOBIA (Geographic Object-based Image Analysis), heir of OBIA (Object-based Image Analysis), is regarded as an important methodology by object-oriented paradigm for remote sensing, dealing with geo-objects related to image segmentation and classification in the different view point of pixel-based processing. This also helps to directly link to GIS applications. Thus, GEOBIA software is on the booming. The main theme of this study is to look into the applicability of geo-spatial processing open source to GEOBIA. However, there is no few fully featured open source for GEOBIA which needs complicated schemes and algorithms, till It was carried out to implement a preliminary system for GEOBIA running an integrated and user-oriented environment. This work was performed by using various open sources such as OTB or PostgreSQL/PostGIS. Some points are different from the widely-used proprietary GEOBIA software. In this system, geo-objects are not file-based ones, but tightly linked with GIS layers in spatial database management system. The mean shift algorithm with parameters associated with spatial similarities or homogeneities is used for image segmentation. For classification process in this work, tree-based model of hierarchical network composing parent and child nodes is implemented by attribute join in the semi-automatic mode, unlike traditional image-based classification. Of course, this integrated GEOBIA system is on the progressing stage, and further works are necessary. It is expected that this approach helps to develop and to extend new applications such as urban mapping or change detection linked to GIS data sets using GEOBIA.

• 한국농공학회논문집
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• 제53권2호
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• pp.19-26
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• 2011

SATEEC ArcView GIS system was developed using the Universal Soil Loss Equation (USLE) and sediment delivery ratio (SDR) modules. In addition, time-variant R and C modules and $R_5$ module were developed and integrated into the SATEEC system in recent years. The SATEEC ArcView GIS 2.1 system is a simple-to-use system which can estimate soil erosion and sediment yield spatially and temporarily using only USLE input data, DEM, and daily rainfall dataset. In this study, the SATEEC 2.1 system was used to evaluate the effects of USLE LS input data considering slope length segmentation on soil erosion and sediment yield estimation. Use of USLE LS with slope length segmentation due to roads in the watershed, soil erosion estimation decreased by 24.70 %. However, the estimated sediment yield using SATEEC GA-SDR matched measured sediment values in both scenarios (EI values of 0.650 and EI 0.651 w/o and w/flow segmentation). This is because the SATEEC GA-SDR module estimates lower SDR in case of greater soil erosion estimation (without flow length segmentation) and greater SDR in case of lower soil erosion estimation (with flow length segmentation). This indicates that the SATEEC soil erosion need to be estimated with care for accurate estimation of SDR at a watershed scale and for accurate evaluation of BMPs in the watershed.

• 한국멀티미디어학회논문지
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• 제25권5호
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• pp.757-768
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• 2022

In this paper, we proposed a system that visualizes a hologram device in 3D by utilizing the CT image segmentation function based on artificial intelligence deep learning. The input axial CT medical image is converted into Sagittal and Coronal, and the input image and the converted image are divided into 3D volumes using ResUNet, a deep learning model. In addition, the volume is created by segmenting the tumor region in the segmented liver image. Each result is integrated into one 3D volume, displayed in a medical image viewer, and converted into a video. When the converted video is transmitted to the hologram device and output from the device, a 3D image with a sense of space can be checked. As for the performance of the deep learning model, in Axial, the basic input image, DSC showed 95.0% performance in liver region segmentation and 67.5% in liver tumor region segmentation. If the system is applied to a real-world care environment, additional physical contact is not required, making it safer for patients to explain changes before and after surgery more easily. In addition, it will provide medical staff with information on liver and liver tumors necessary for treatment or surgery in a three-dimensional manner, and help patients manage them after surgery by comparing and observing the liver before and after liver resection.

• 한국정보통신학회논문지
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• 제17권2호
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• pp.447-452
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• 2013

미세유체소자(microfluidic device)는 미생물과 관련된 다양한 작업들에 대해서 정확한 제어를 제공할 수 있다. 본 논문에서는 미세유체 소자와 디지털 홀로그래피 마이크로스코피 기술로 구성된 시스템을 구성하고 미생물의 3D 이미징과 세그먼테이션을 설명한다. 각각의 미생물은 미세유체 채널을 통하여 흘러가며 홀로그래피 마이크로스코피가 홀로그램을 기록한다. 기록된 홀로그램은 Fresnel 변환을 통하여 컴퓨터적으로 복원되며, 복원된 영상의 위상성분을 이용하여 미생물의 위치 정보를 찾기 위한 세그먼테이션을 수행한다. 제안하는 방법의 유용함을 설명하기 위하여 광학 실험을 수행하고 그 결과를 나타내었다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제17권10호
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• pp.2788-2808
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• 2023

Brain tumors are one of the most threatening malignancies for humans. Misdiagnosis of brain tumors can result in false medical intervention, which ultimately reduces a patient's chance of survival. Manual identification and segmentation of brain tumors from Magnetic Resonance Imaging (MRI) scans can be difficult and error-prone because of the great range of tumor tissues that exist in various individuals and the similarity of normal tissues. To overcome this limitation, the Amended Convolutional Neural Network (ACNN) model has been introduced, a unique combination of three techniques that have not been previously explored for brain tumor detection. The three techniques integrated into the ACNN model are image tissue preprocessing using the Kalman Bucy Smoothing Filter to remove noisy pixels from the input, image tissue segmentation using the Isotonic Regressive Image Tissue Segmentation Process, and feature extraction using the Marr Wavelet Transformation. The extracted features are compared with the testing features using a sigmoid activation function in the output layer. The experimental findings show that the suggested model outperforms existing techniques concerning accuracy, precision, sensitivity, dice score, Jaccard index, specificity, Positive Predictive Value, Hausdorff distance, recall, and F1 score. The proposed ACNN model achieved a maximum accuracy of 98.8%, which is higher than other existing models, according to the experimental results.