• 한국컴퓨터정보학회논문지
• /
• 제17권2호
• /
• pp.49-57
• /
• 2012

FCM(fuzzy c-means)은 일반적으로 영상 분할에서 좋은 성능을 보인다. 하지만 공간 정보를 사용하지 않는 일반적인 FCM 알고리즘은 낮은 대비의 영상, 경계선이 뚜렷하지 않은 영상, 잡음이 포함된 영상의 분할에는 좋지 않은 성능을 보인다. 이와 같은 문제를 해결하기 위해 본 논문에서는 3x3 크기의 윈도우를 이용하여 윈도우 내의 중심 픽셀과 주변 픽셀간의 거리 정보를 소속 함수에 추가한 개선된 공간적 퍼지 클러스터링 알고리즘을 제안한다. 본 논문에서는 분할 계수, 분할 엔트로피, Xie-Bdni 함수와 같은 클러스터링 검증 함수를 이용하여 FCM 기반의 다양한 클러스터링 알고리즘과 제안한 알고리즘과의 성능을 비교하였다. 성능 평가 결과 제안한 알고리즘이 기존의 FCM기반의 클러스터링 알고리즘보다 클러스터링 검증 함수에서 성능이 우수함을 확인 할 수 있었다.

• 한국농공학회논문집
• /
• 제64권1호
• /
• pp.15-26
• /
• 2022

3D open-field farm model developed from UAV (Unmanned Aerial Vehicle) data could make crop monitoring easier, also could be an important dataset for various fields like remote sensing or precision agriculture. It is essential to separate crops from the non-crop area because labeling in a manual way is extremely laborious and not appropriate for continuous monitoring. We, therefore, made a 3D open-field farm model based on UAV images and developed a crop segmentation model using a supervised machine learning algorithm. We compared performances from various models using different data features like color or geographic coordinates, and two supervised learning algorithms which are SVM (Support Vector Machine) and KNN (K-Nearest Neighbors). The best approach was trained with 2-dimensional data, ExGR (Excess of Green minus Excess of Red) and z coordinate value, using KNN algorithm, whose accuracy, precision, recall, F1 score was 97.85, 96.51, 88.54, 92.35% respectively. Also, we compared our model performance with similar previous work. Our approach showed slightly better accuracy, and it detected the actual crop better than the previous approach, while it also classified actual non-crop points (e.g. weeds) as crops.

• Journal of Information Processing Systems
• /
• 제18권4호
• /
• pp.549-561
• /
• 2022

In the Khmer writing system, the Khmer script is the official letter of Cambodia, written from left to right without a space separator; it is complicated and requires more analysis studies. Without clear standard guidelines, a space separator in the Khmer language is used inconsistently and informally to separate words in sentences. Therefore, a segmented method should be discussed with the combination of the future Khmer natural language processing (NLP) to define the appropriate rule for Khmer sentences. The critical process in NLP with the capability of extensive data language analysis necessitates applying in this scenario. One of the essential components in Khmer language processing is how to split the word into a series of sentences and count the words used in the sentences. Currently, Microsoft Word cannot count Khmer words correctly. So, this study presents a systematic library to segment Khmer phrases using the bi-directional maximal matching (BiMM) method to address these problematic constraints. In the BiMM algorithm, the paper focuses on the Bidirectional implementation of forward maximal matching (FMM) and backward maximal matching (BMM) to improve word segmentation accuracy. A digital or prefix tree of data structure algorithm, also known as a trie, enhances the segmentation accuracy procedure by finding the children of each word parent node. The accuracy of BiMM is higher than using FMM or BMM independently; moreover, the proposed approach improves dictionary structures and reduces the number of errors. The result of this study can reduce the error by 8.57% compared to FMM and BFF algorithms with 94,807 Khmer words.

• 한국HCI학회:학술대회논문집
• /
• 한국HCI학회 2009년도 학술대회
• /
• pp.193-196
• /
• 2009

단일 영상에서 피부색 영역을 추출하기 위해서 기존의 많은 방법들이 하나의 고정된 피부색 모델을 사용한다. 그러나 영상에 특성에 따라 영상에 포함된 피부색의 분포가 다양하기 때문에 이러한 방법을 이용하여 피부색을 검출할 경우 낮은 검출율이나 높은 긍정 오류율이 발생할 수 있다. 따라서 영상의 특징에 따라 적응적으로 피부색 영역을 추출할 수 있는 방법이 필요하다. 이에 본 논문에서는 영상의 특징에 따라 2단계의 과정을 거쳐 피부색 모델을 수정하는 방법으로, 다양한 조명과 환경 조건에서 높은 검출율과 낮은 긍정 오류율을 동시에 가지는 알고리즘을 제안한다.

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

• 한국방송∙미디어공학회:학술대회논문집
• /
• 한국방송공학회 2011년도 하계학술대회
• /
• pp.437-438
• /
• 2011

Spectral segmentation is a major trend in image segmentation. Specially, constrained spectral segmentation, inspired by the user-given inputs, remains its challenging task. Since it makes use of the spectrum of the affinity matrix of a given image, its overall quality depends mainly on how to design the graphical model. In this work, we propose a sparse, multi-layer graphical model, where the pixels and the over-segmented regions are the graph nodes. Here, the graph affinities are computed by using the must-link and cannot-link constraints as well as the likelihoods that each node has a specific label. They are then used to simultaneously cluster all pixels and regions into visually coherent groups across all layers in a single multi-layer framework of Normalized Cuts. Although we incorporate only the adjacent connections in the multi-layer graph, the foreground object can be efficiently extracted in the spectral framework. The experimental results demonstrate the relevance of our algorithm as compared to existing popular algorithms.

• 한국멀티미디어학회논문지
• /
• 제7권3호
• /
• pp.319-326
• /
• 2004

본 논문에서는 물체의 경계선을 고려한 칼라 영상 분할 방법을 제안한다. 제안 방법은 먼저 원영상을 벡터 양자화한 후 양자화된 영상의 인덱스 맵을 이용하여 초기 영역을 설정하였다. 그 후 HSI컬러 공간을 이용한 영역 병합에서 물체의 경계선을 고려하기 위해 경계선 제한 성분을 적용하여 영역들을 병합하였다. 또한 RGB 컬러 공간을 이용하여 HSI 컬러 공간에서 병합되지 않은 영역들을 병합하였다. 그리 고 영역병합 알고리즘을 통해 반복적인 처리를 감소시킴으로써 처리 시간을 줄였다. 실험 결과에서는 다양한 영상에 대해 주요 영역들의 분할 결과 및 처리소요시간에서 우수한 성능을 보였다.

• 전자공학회논문지B
• /
• 제28B권7호
• /
• pp.566-577
• /
• 1991

In this paper, the method of image segmentation based on a pyramid of reduced resolution versions of the input input image is persented. In a pyramid structure, two regions (a given pixel and its mother pixels) are compared by the proposed inverse-mother-son variance ratio (IMSVR) method for the detection of an optinal object pixel and are determined whether they are similar enough to be viewed as one region or disparate to be viewed as ditinct regions By the proposed method, an l`timal object pixel has been setectedat some level, it is necessary to retrieve its boundary precisely. Moving down the pyramid to levels of higher resolution is requires. In this paper, the top-sown pyramid traversing algorithm for an image segmentation using a pyrmid structure is presented. Using the computer simulation, the results by the proposed statistical method and object traversing method are investigated for the binary image and the real image at the results of computer simulation, the proposed method of image segmentation based on a pyramid structure seem to have useful properties and deserve consideration as a possible alternative to existing methods of omage segmentation. The computation for the proposed method is required 0 (log n), for an TEX>$n{\times}n$ input image.

• 정보처리학회논문지B
• /
• 제8B권5호
• /
• pp.493-500
• /
• 2001

동영상 데이터의 장면 분할은 내용기반 분석을 위해 필요한 기초작업이다. 본 논문에서는 동영상의 매 프레임을 워터쉐드 알고리즘을 통해 객체 중심의 작은 영역들로 나누어 각 영역이 연속적인 프레임 상에서 계속 존재하는가를 파악하는 방법을 통해 장면을 구분하는 새로운 영역기반 장면 분할 기법을 제안한다. 이를 위해 각 영역들에 대한 형태와 공간상의 유사도를 측정해 영역들의 움직임 정도에 따라 동영상 데이터를 동적 구간과 정적 구간으로 나누고 인접한 구간간의 유사도에 따라 그룹화 하는 방법을 통해 장면 분할을 시도한다. 제안하는 기법은 객체들을 표현하는 각 영역을 비교 대상으로 삼기 때문에 명암 변화나 변화에도 오검출 하지 않으면서 효과적으로 장면을 구분해낼수 있는 장점을 갖는다.

• 한국정보통신학회:학술대회논문집
• /
• 한국정보통신학회 2012년도 추계학술대회
• /
• pp.874-877
• /
• 2012

This paper proposes the new method based on contour following method with directional angle to segment the cell into the nuclei. The object image was the Thyroid Gland cell image that was diagnosed as normal and abnormal(two types of abnormal : follicular neoplastic cell, and papillary neoplastic cell), respectively. The nuclei were successfully diagnosed as normal and abnormal. this paper, improved method of digital image analysis required in basic medical science for diagnosis of cells was proposed. The object image was the Thyroid Gland cell image with difference of chromatin patterns. To segment the cell nucleus from background, the region segmentation algorithm by edge tracing was proposed. And feature parameter was obtained from discrete Fourier transformation of image. After construct a feature sample group of each cells, experiment of discrimination was executed with any verification cells. As a result of experiment using features proposed in this paper, get a better segmentation rate(70-90%) than previously reported papers, and this method give shape to get objectivity and fixed quantity in diagnosis of cells. The methods described in this paper be used immediately for discrimination of neoplastic cells.