• 대한전기학회:학술대회논문집
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• 대한전기학회 1995년도 하계학술대회 논문집 B
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• pp.974-976
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• 1995

Segmentation of image data is an important problem in computer vision, remote sensing, and image analysis. Most objects in the real world have textured surfaces. Segmentation based on texture information is possible even if there are no apparent intensity edges between the different regions. There are many existing methods for texture segmentation and classification, based on different types of statistics that can be obtained from the gray-level images. In this paper, we use a neural network model --- ART-2 (Adaptive Resonance Theory) for textures in an image, proposed by Carpenter and Grossberg. In our experiments, we use Walsh matrix as feature value for textured image.

• 대한원격탐사학회:학술대회논문집
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• 대한원격탐사학회 2003년도 Proceedings of ACRS 2003 ISRS
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• pp.153-155
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• 2003

Laser scanning has become a viable technique for the collection of a large amount of accurate 3D point data densely distributed on the scanned object surface. The inherent 3D nature of the sub-randomly distributed point cloud provides abundant spatial information. To explore valuable spatial information from laser scanned data becomes an active research topic, for instance extracting digital elevation model, building models, and vegetation volumes. The sub-randomly distributed point cloud should be segmented and classified before the extraction of spatial information. This paper investigates some exist segmentation methods, and then proposes an octree-based split-and-merge segmentation method to divide lidar data into clusters belonging to 3D planes. Therefore, the classification of lidar data can be performed based on the derived attributes of extracted 3D planes. The test results of both ground and airborne lidar data show the potential of applying this method to extract spatial features from lidar data.

• Journal of Information Processing Systems
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• 제14권6호
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• pp.1405-1419
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• 2018

An image fusion method is proposed on the basis of depth model segmentation to overcome the shortcomings of noise interference and artifacts caused by infrared and visible image fusion. Firstly, the deep Boltzmann machine is used to perform the priori learning of infrared and visible target and background contour, and the depth segmentation model of the contour is constructed. The Split Bregman iterative algorithm is employed to gain the optimal energy segmentation of infrared and visible image contours. Then, the nonsubsampled contourlet transform (NSCT) transform is taken to decompose the source image, and the corresponding rules are used to integrate the coefficients in the light of the segmented background contour. Finally, the NSCT inverse transform is used to reconstruct the fused image. The simulation results of MATLAB indicates that the proposed algorithm can obtain the fusion result of both target and background contours effectively, with a high contrast and noise suppression in subjective evaluation as well as great merits in objective quantitative indicators.

• 대한음성학회지:말소리
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• 제58호
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• pp.67-81
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• 2006

For a large corpus of time-aligned data, HMM based approaches are most widely used for automatic segmentation, providing a consistent and accurate phone labeling scheme. There are two methods for training in HMM. Flat starting method has a property that human interference is minimized but it has low accuracy. Bootstrap method has a high accuracy, but it has a defect that manual segmentation is required In this paper, a new algorithm is proposed to minimize manual work and to improve the performance of automatic segmentation. At first phase, voiced, unvoiced and silence classification is performed for each speech data frame. At second phase, the phoneme sequence is aligned dynamically to the voiced/unvoiced/silence sequence according to the acoustic phonetic rules. Finally, using these segmented speech data as a bootstrap, phoneme model parameters based on HMM are trained. For the performance test, hand labeled ETRI speech DB was used. The experiment results showed that our algorithm achieved 10% improvement of segmentation accuracy within 20 ms tolerable error range. Especially for the unvoiced consonants, it showed 30% improvement.

• 한국인터넷방송통신학회논문지
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• 제19권6호
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• pp.227-233
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• 2019

최근 딥러닝 기술의 발달과 함께 신경 네트워크는 컴퓨터 비전에서도 성공을 거두고 있다. 컨볼루션 신경망은 단순한 영상 분류 작업뿐만 아니라 객체 분할 및 검출 등 난이도가 높은 작업에서도 탁월한 성능을 보였다. 그러나 그러한 많은 심층 학습 모델은 지도학습에 기초하고 있으며, 이는 이미지 라벨보다 주석 라벨이 더 많이 필요하다. 특히 semantic segmentation 모델은 훈련을 위해 픽셀 수준의 주석을 필요로 하는데, 이는 매우 중요하다. 이 논문은 이러한 문제를 해결하기 위한 네트워크 훈련을 위해 영상 수준 라벨만 필요한 약지도 semantic segmentation 방법을 제안한다. 기존의 약지도학습 방법은 대상의 특정 영역만 탐지하는 데 한계가 있다. 반면에, 본 논문에서는 우리의 모델이 사물의 더 다른 부분을 인식하도 multi-classifier 심층 학습 아키텍처를 사용한다. 제안된 방법은 VOC 2012 검증 데이터 세트를 사용하여 평가한다.

• 한국항행학회논문지
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• 제16권5호
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• pp.846-852
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• 2012

영상분할 결과는 알고리즘에 관련된 매개변수들에 따라 다르기 때문에 최적 분할을 위하여 시행 착오법이 많이 이용된다. 본 논문에서는 3차원 변량 분석법을 이용하여 영역기반 active contour 방법에 관련된 최적 매개변수들을 결정하는 방법을 제안한다. 3원 변량 분석법에 의해서 추출된 결과와 사용자가 영상에서 직접 그린 결과가 상호 비교된다. 마지막으로 각 매개변수들의 주요 효과와 상호작용 효과를 측정하고 최적 값을 추출하기 위하여 점 추정 및 구간 추정 값을 계산한다. 본 논문에서 제안한 방법은 구간 상수 모델을 대상으로 영상분할시 최적 매개변수들을 추출하는데 큰 도움을 줄 것이다.

• 한국멀티미디어학회논문지
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• 제23권3호
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• pp.468-475
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• 2020

In this paper, we proposed a three-dimensional visualization system for medical images in augmented reality based on deep learning. In the proposed system, the artificial neural network model performed fully automatic segmentation of the region of lung and pulmonary nodule from chest CT images. After applying the three-dimensional volume rendering method to the segmented images, it was visualized in augmented reality devices. As a result of the experiment, when nodules were present in the region of lung, it could be easily distinguished with the naked eye. Also, the location and shape of the lesions were intuitively confirmed. The evaluation was accomplished by comparing automated segmentation results of the test dataset to the manual segmented image. Through the evaluation of the segmentation model, we obtained the region of lung DSC (Dice Similarity Coefficient) of 98.77%, precision of 98.45%, recall of 99.10%. And the region of pulmonary nodule DSC of 91.88%, precision of 93.05%, recall of 90.94%. If this proposed system will be applied in medical fields such as medical practice and medical education, it is expected that it can contribute to custom organ modeling, lesion analysis, and surgical education and training of patients.

• 로봇학회논문지
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• 제15권4호
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• pp.316-322
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• 2020

Endoscopic spine surgery is an advanced surgical technique for spinal surgery since it minimizes skin incision, muscle damage, and blood loss compared to open surgery. It requires, however, accurate positioning of an endoscope to avoid spinal nerves and to locate the endoscope near the target disk. Before the insertion of the endoscope, a guide needle is inserted to guide it. Also, the result of the surgery highly depends on the surgeons' experience and the patients' CT or MRI images. Thus, for the training, a number of haptic simulators for spinal needle insertion have been developed. But, still, it is difficult to be used in the medical field practically because previous studies require manual segmentation of vertebrae from CT images, and interaction force between the needle and soft tissue has not been considered carefully. This paper proposes AI-based automatic vertebrae CT-image segmentation and haptic rendering method using the proposed need-tissue interaction model. For the segmentation, U-net structure was implemented and the accuracy was 93% in pixel and 88% in IoU. The needle-tissue interaction model including puncture force and friction force was implemented for haptic rendering in the proposed spinal needle insertion simulator.

• 전자공학회논문지C
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• 제36C권9호
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• pp.66-75
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• 1999

영상분할은 입력된 영상을 처리하여 유사한 화소들의 집합인 영역들로 화소들을 구분하는 작업이다. 영상분할의 결과는 영상인식의 정확성에 큰 영향을 미친다. 본 논문에서는 유전자 알고리즘을 이용하여 마르코프 랜덤 필드(Markov random field)에 기반한 영상분할 방법을 제안한다. 제안한 방법에서는 잡음과 흔들림(blurring)에 강한 MRF를 이용하여 영상을 모델링 한다. HRF기반 영상분할 방법은 왜곡에 강한 반면, 정확한 파라미터의 추정이 요구된다. 그래서 , 추정방법으로 많은 파라미터를 포함하는 문제를 다루는데 효율적인 유전자 알고리즘을 사용한다. 실 영상을 가지고 수행된 실험 결과와 자동 차량 추출 시스템에의 응용결과는 제안된 방법의 효율성을 보여준다.

• 방송공학회논문지
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• 제3권1호
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• pp.50-60
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• 1998

본 논문에서는 콤팩트한 동영상 표현과 객체기반의 generic한 동영상압축을 위한 파라미터릭 움직임 모델의 파라미터 추정과 세그맨테이션 기법에 관해서 기술한다. 동영상의 optical flow와 같은 국소적 움직임 정보와 파라미터 움직임 모델의 특징을 이용해서 영상의 콤팩트한 구조적 표현을 추출하기 위해, 본 논문에서는 2 스템의 과정 즉, 초기영역을 추출하는 과정과, 파라미터릭 움직임 파라미터의 추정과 세그맨테이션을 동시에 수행하는 과정으로 구성된 새로운 알고리즘을 제안한다. 혼합 모델이 ML 추정에 의거한 확률적 클러스터링에 의해 움직임 물체의 움직임과 형상을 반영한 초기영역을 추출하고, 파라미터릭 움직임 모델을 사용해서 각각의 초기 영역마다 움직임 파라미터를 추정하고 세그맨테이션을 수행한다. 또한, CIF 표준 동영상을 사용한 모의 실험을 통해 본 제안 알고리즘의 유효성을 평가한다.