• Journal of Information Processing Systems
• /
• 제9권4호
• /
• pp.592-601
• /
• 2013

Object segmentation is a challenging task in image processing and computer vision. In this paper, we present a visual attention based segmentation method to segment small sized interesting objects in natural images. Different from the traditional methods, we first search the region of interest by using our novel saliency-based method, which is mainly based on band-pass filtering, to obtain the appropriate frequency. Secondly, we applied the Gaussian Mixture Model (GMM) to locate the object region. By incorporating the visual attention analysis into object segmentation, our proposed approach is able to narrow the search region for object segmentation, so that the accuracy is increased and the computational complexity is reduced. The experimental results indicate that our proposed approach is efficient for object segmentation in natural images, especially for small objects. Our proposed method significantly outperforms traditional GMM based segmentation.

• Journal of Information Processing Systems
• /
• 제16권6호
• /
• pp.1424-1436
• /
• 2020

Although huge progress has been made in current image segmentation work, there are still no efficient segmentation strategies for tree image which is taken from natural environment and contains complex background. To improve those problems, we propose a method for tree image segmentation combining adaptive mean shifting with image abstraction. Our approach perform better than others because it focuses mainly on the background of image and characteristics of the tree itself. First, we abstract the original tree image using bilateral filtering and image pyramid from multiple perspectives, which can reduce the influence of the background and tree canopy gaps on clustering. Spatial location and gray scale features are obtained by step detection and the insertion rule method, respectively. Bandwidths calculated by spatial location and gray scale features are then used to determine the size of the Gaussian kernel function and in the mean shift clustering. Furthermore, the flood fill method is employed to fill the results of clustering and highlight the region of interest. To prove the effectiveness of tree image abstractions on image clustering, we compared different abstraction levels and achieved the optimal clustering results. For our algorithm, the average segmentation accuracy (SA), over-segmentation rate (OR), and under-segmentation rate (UR) of the crown are 91.21%, 3.54%, and 9.85%, respectively. The average values of the trunk are 92.78%, 8.16%, and 7.93%, respectively. Comparing the results of our method experimentally with other popular tree image segmentation methods, our segmentation method get rid of human interaction and shows higher SA. Meanwhile, this work shows a promising application prospect on visual reconstruction and factors measurement of tree.

• 한국멀티미디어학회논문지
• /
• 제15권12호
• /
• pp.1417-1429
• /
• 2012

In neuroscientific research, image segmentation is one of the most important processes. The morphology of axons plays an important role for researchers seeking to understand axonal functions and connectivity. In this study, we evaluated the level set segmentation method for neuronal axons in a Brainbow confocal microscopy image. We first obtained a reconstructed image on an x-z plane. Then, for preprocessing, we also applied two methods: anisotropic diffusion filtering and bilateral filtering. Finally, we performed image segmentation using the level set method with three different approaches. The accuracy of segmentation for each case was evaluated in diverse ways. In our experiment, the combination of bilateral filtering with the level set method provided the best result. Consequently, we confirmed reasonable results with our approach; we believe that our method has great potential if successfully combined with other research findings.

• The Journal of the Acoustical Society of Korea
• /
• 제28권2E호
• /
• pp.72-78
• /
• 2009

Heart sound segmentation into its components, S1, systole, S2 and diastole is the first step of analysis and the most important part in the automatic diagnosis of heart sounds. Conventionally, the Shannon energy envelope peak detection method has been popularly used due to its superior performance in locating S1 and S2. Recently, the HMM has been shown to be quite suitable in modeling the heart sound signal and its use in segmenting the heart sound signal has been suggested with some success. In this paper, we compared the two methods for heart sound segmentation using a common database. Experimental tests carried out on the 4 different types of heart sound signals showed that the segmentation accuracy relative to the manual segmentation was 97.4% in the HMM based method which was larger than 91.5% in the peak detection method.

• 한국군사과학기술학회지
• /
• 제20권1호
• /
• pp.40-47
• /
• 2017

To classify the vehicle targets automatically using thermal images, there are usually two main categories of feature extraction method, local and shape feature extraction methods. Since thermal images have less texture information than color images, the shape feature extraction method is useful when the segmentation results are correct. However, if there are some errors in target segmentation, the shape feature may contain some errors, then the classification accuracy can be decreased. To overcome these problems, in this paper, we propose the segmentation reliability estimation method for target classification. The segmentation reliability can be estimated by using the difference information of average intensities and edge energies between the target and the background area. The estimated segmentation reliability is applied in the decision level fusion method of classification results using both shape and local features. Experiment results using the thermal images of the vehicle targets (main battle tank, armored personnel carrier, military truck, and an estate car) show that the proposed classification method and the segmentation reliability estimation method have a good performance in classification accuracy.

• Journal of International Society for Simulation Surgery
• /
• 제4권1호
• /
• pp.21-23
• /
• 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.

• 방송공학회논문지
• /
• 제18권4호
• /
• pp.609-618
• /
• 2013

본 논문은 기존 그래프 컷 기반 영상분할의 성능은 유지하면서 연산속도가 빠른 영상분할 방법을 제안한다. 기존 그래프 컷 기반 영상분할은 높은 성능을 보이지만 고유쌍 연산으로 인해 분할 속도가 느리다는 단점을 지닌다. 이는 고유쌍 연산에서 영상 내 모든 화소 사이의 유사도를 고려하여 정방행렬을 만들기 때문이다. 그러므로 제안하는 방법은 영상을 여러 영역으로 분할하여 작은 크기의 정방행렬을 구성하고 이를 통해 고유쌍 연산 속도를 크게 향상시킨다. 본 논문에서는 대수적 다중 격자를 이용한 다중스케일 영상분할법을 제안하고 실험 결과를 통해 제안하는 방법이 기존 영상분할 방법보다 그 성능이 더 우수함을 보인다.

• 한국멀티미디어학회논문지
• /
• 제17권3호
• /
• pp.342-352
• /
• 2014

본 논문에서는 마이크로 CT 영상에서 치밀뼈와 해면뼈의 자동 분할 방법을 제안하고 분할된 해면뼈의 형태학적 분석 방법의 구현에 대해 기술한다. 제안된 분할 방법에서는 임계값을 이용하여 뼈 영역을 추출한다. 그 다음에는, 뼈의 바깥 경계선부터 안쪽 방향으로 인접한 경계선을 찾아 치밀뼈 후보 영역을 설정한다. 치밀뼈 후보 영역들 중에서 평균 픽셀값이 최대가 되는 지점을 후보 영역을 탐색하여 치밀뼈를 제거한다. 분할된 해면뼈에 BV/TV, Tb.Th, Tb.Sp, Tb.N의 네 가지 형태학적 지표자들을 계산하는 방법을 VTK(Visualization ToolKit)와 구 정합 알고리즘을 이용하여 구현하였다. 구현된 방법을 쥐의 20개 대퇴골 근위부 영상에 적용하였으며 사람이 수작업으로 분할하는 방법과 비교 실험을 수행하였다. 실험 결과 네 가지 형태학적 지표자 모두 수작업으로 분할한 경우와 자동으로 분할한 경우 3% 이내의 평균 오차율을 보여 제안된 방법은 번거로운 수작업 분할 대신 사용될 수 있음을 알 수 있었다.

• 한국정밀공학회:학술대회논문집
• /
• 한국정밀공학회 2013년도 춘계학술대회 논문집
• /
• pp.715-716
• /
• 2013

• 대한의용생체공학회:의공학회지
• /
• 제42권4호
• /
• pp.159-166
• /
• 2021

In order to analyze cell images, accurate segmentation of each cell is indispensable. However, the reality is that accurate cell image segmentation is not easy due to various noises, dense cells, and inconsistent shape of cells. Therefore, in this paper, we propose an algorithm that combines marker-based watershed segmentation and ellipse fitting method for glioblastoma cell segmentation. In the proposed algorithm, in order to solve the over-segmentation problem of the existing watershed method, the marker-based watershed technique is primarily performed through "seeding using local minima". In addition, as a second process, the concave point search using ellipse fitting for final segmentation based on the connection line between the concave points has been performed. To evaluate the performance of the proposed algorithm, we compared three algorithms with other algorithms along with the calculation of segmentation accuracy, and we applied the algorithm to other cell image data to check the generalization and propose a solution.