Segment of Multiple Objects Based on Parameter Active Contour Model

Liu Hongshen,Wang Nan,Zhang Xuefeng 보안공학연구지원센터 2016 International Journal of Signal Processing, Image Vol.9 No.12

The subject of this paper is the segmentation of multiple objects from images based on 6the parameter active contour model (PACM). After analyzing application of the parameter active model to segment multiple objects, the evolution strategies and disadvantages of existing methods are presented. This paper proposes that the key points are two parts in detecting multiple objects with the PACM in the shrinking strategy. One key point includes the split time where contours appear as self-crosses, and the split algorithm of contours. The other key point is to maintain the uniform distribution of sampling points on contours in order to match the shapes of objects in segmenting. A new algorithm for detecting self-crosses is presented, and the results show that the new algorithm is faster than the other algorithm. The problem where vertexes on contours are sampled to match the shapes of objects in segmenting is studied, and its solution is presented.

활성 윤곽선 모델을 이용한 얼굴 경계선 추출

장재식,김은이,김항준,Chang Jae Sik,Kim Eun Yi,Kim Hang Joon 대한전자공학회 2005 電子工學會論文誌-CI (Computer and Information) Vol.42 No.1

본 논문에서는 복잡한 환경에서 정확한 얼굴영역의 경계를 추출하기 위한 활성 윤곽선 모델(Active Contour Model)을 제안한다. 제안된 모델에서 윤곽선은 레벨 함수 φ의 제로 레벨 집합으로 표현되고, 레벨 집합의 편미분 방정식을 통해 진화된다. 이 때, 제안된 모델에서는 윤곽선의 진화와 종교를 위해 2차원 가우시안 모델로 표현되는 피부색 정보를 이용한다. 이를 통해 잡음 및 다양한 포즈를 가지는 복잡한 영상에서도 정확한 얼굴 경계선을 얻을 수 있는 강건한 추출 방법이 구현된다. 제안된 방법의 유효성을 평가하기 위해서 다양한 영상에 대해서 실험이 이루어졌으며, 그 결과를 geodesic 활성 윤곽선 모델의 결과와 비교하였다. 실험결과는 제안된 방법의 보다 나은 성능을 보여준다. This paper presents an active contour model for extracting accurate facial regions in complex environments. In the model, a contour is represented by a zero level set of level function φ, and evolved via level set partial differential equations. Then, unlike general active contours, skin color information that is represented by 2D Gaussian model is used for evolving and slopping a curve, which allows the proposed method to be robust to noise and varying pose. To assess the effectiveness of the proposed method it was tested with several natural scenes, and the results were compared with those of geodesic active contours. Experimental results demonstrate the superior performance of the proposed method.

Saliency Detection based on Global Color Distribution and Active Contour Analysis

( Zhengping Hu ),( Zhenbin Zhang ),( Zhe Sun ),( Shuhuan Zhao ) 한국인터넷정보학회 2016 KSII Transactions on Internet and Information Syst Vol.10 No.12

In computer vision, salient object is important to extract the useful information of foreground. With active contour analysis acting as the core in this paper, we propose a bottom-up saliency detection algorithm combining with the Bayesian model and the global color distribution. Under the supports of active contour model, a more accurate foreground can be obtained as a foundation for the Bayesian model and the global color distribution. Furthermore, we establish a contour-based selection mechanism to optimize the global-color distribution, which is an effective revising approach for the Bayesian model as well. To obtain an excellent object contour, we firstly intensify the object region in the source gray-scale image by a seed-based method. The final saliency map can be detected after weighting the color distribution to the Bayesian saliency map, after both of the two components are available. The contribution of this paper is that, comparing the Harris-based convex hull algorithm, the active contour can extract a more accurate and non-convex foreground. Moreover, the global color distribution can solve the saliency-scattered drawback of Bayesian model, by the mutual complementation. According to the detected results, the final saliency maps generated with considering the global color distribution and active contour are much-improved.

Saliency Detection based on Global Color Distribution and Active Contour Analysis

Hu, Zhengping,Zhang, Zhenbin,Sun, Zhe,Zhao, Shuhuan Korean Society for Internet Information 2016 KSII Transactions on Internet and Information Syst Vol.10 No.12

In computer vision, salient object is important to extract the useful information of foreground. With active contour analysis acting as the core in this paper, we propose a bottom-up saliency detection algorithm combining with the Bayesian model and the global color distribution. Under the supports of active contour model, a more accurate foreground can be obtained as a foundation for the Bayesian model and the global color distribution. Furthermore, we establish a contour-based selection mechanism to optimize the global-color distribution, which is an effective revising approach for the Bayesian model as well. To obtain an excellent object contour, we firstly intensify the object region in the source gray-scale image by a seed-based method. The final saliency map can be detected after weighting the color distribution to the Bayesian saliency map, after both of the two components are available. The contribution of this paper is that, comparing the Harris-based convex hull algorithm, the active contour can extract a more accurate and non-convex foreground. Moreover, the global color distribution can solve the saliency-scattered drawback of Bayesian model, by the mutual complementation. According to the detected results, the final saliency maps generated with considering the global color distribution and active contour are much-improved.

An Adaptive Stopping Active Contour Model for Image Segmentation

Yuefeng Niu,Jianzhong Cao,Zuofeng Zhou 대한전기학회 2019 Journal of Electrical Engineering & Technology Vol.14 No.1

Active contour models (ACMs) are widely used in image segmentation applications. However, the selection of maximum iterations which controls the convergence of the ACMs is still a challenging problem. In this paper, an adaptive method for choosing the optimal number of iterations based on the local and global intensity fitting energy is proposed, which increases the automaticity of the active contour model. Moreover, the adoption of the reaction diffusion (RD) method instead of the distance regularization term can improve the accuracy and speed of segmentation effectively. Experimental results on synthetic and real images show that the proposed model outperforms other representative models in terms of accuracy and efficiency.

Adaptive Active Contour Model: a Localized Mutual Information Approach for Medical Image Segmentation

( Shuanglu Dai ),( Shu Zhan ),( Ning Song ) 한국인터넷정보학회 2015 KSII Transactions on Internet and Information Syst Vol.9 No.5

Troubles are often met when traditional active contours extract boundaries of medical images with inhomogeneous bias and various noises. Focusing on such a circumstance, a localized mutual information active contour model is discussed in the paper. By defining neighborhood of each point on the level set, mutual information is introduced to describe the relationship between the zero level set and image field. A driving energy term is then generated by integrating all the information. In addition, an expanding energy and internal energy are designed to regularize the driving energy. Contrary to piecewise constant model, new model has a better command of driving the contours without initialization.

원형에너지가 추가된 p-Snake를 이용한 윤곽선 추출 기법

오승택(Seung-Taek Oh),전병환(Byung-Hwan Jun) 대한전자공학회 2014 전자공학회논문지 Vol.51 No.4

임의의 물체 영상에서 정확한 윤곽선을 찾아내는 것은 영상 처리 관련 시스템을 구축하는데 있어 필수적인 요소이다. 특히, 자동화된 생산 공정에서 생산품의 검사를 위한 비전시스템을 구축하다면 직선, 원 등의 정형화된 모형에 대한 윤곽선의 검출이 매우 중요하다. 본 논문에서는 원형(prototype) 에너지를 추가하여 개선된 윤곽선 추출 알고리즘으로 원형적응 동적윤곽선 모델, p-Snake를 제안한다. 제안 방법은 원형분석을 위하여 물체 영상에 소벨 연산을 수행한 후, 기존 스네이크 알고리즘을 적용하여 초기 윤곽선을 찾는다. 이후 초기 윤곽선 정보에 근거하여 직선, 원 등의 원형(prototype)을 분석하고, 원형 에너지를 정의하여 기존의 스네이크 함수에 추가적인 에너지 항목으로 사용함으로써 물체의 최종 윤곽선을 검출하였다. 산업현장의 배경을 가정한 환경에서 취득된 340장의 영상에 대하여 실험한 결과, 잡음이나 조명 등의 이유로 물체와 배경의 구분이 선명하지 않거나 영상에서 에지가 충분히 존재하지 않는 경우에도 윤곽선을 추출할 수 있음을 확인할 수 있었다. 또한 원형(prototype)과 얼마나 일치하는 가를 나타내는 척도인 유사도의 경우, 제안한 p-ACM으로 추출한 윤곽선의 원형 유사도가 ACM의 처리 결과에 비해 9.85%가량 우수한 것으로 나타났다. It is an essential element for the establishment of image processing related systems to find the exact contour from the image of an arbitrary object. In particular, if a vision system is established to inspect the products in the automated production process, it is very important to detect the contours for standardized shapes such lines and curves. In this paper, we propose a prototype adaptive dynamic contour model, p-Snake with improved contour extraction algorithms by adding the prototype energy. The proposed method is to find the initial contour by applying the existing Snake algorithm after Sobel operation is performed for prototype analysis. Next, the final contour of the object is detected by analyzing prototypes such as lines and circles, defining prototype energy and using it as an additional energy item in the existing Snake function on the basis of information on initial contour. We performed experiments on 340 images obtained by using an environment that duplicated the background of an industrial site. It was found that even if objects are not clearly distinguished from the background due to noise and lighting or the edges being insufficiently visible in the images, the contour can be extracted. In addition, in the case of similarity which is the measure representing how much it matches the prototype, the prototype similarity of contour extracted from the proposed p-ACM is superior to that of ACM by 9.85%.

Detection of Pulmonary Region in Medical Images through Improved Active Control Model

Kwon Yong-Jun,Won Chul-Ho,Kim Dong-Hun,Kim Pil-Un,Park Il-Yong,Park Hee-Jun,Lee Jyung-Hyun,Kim Myoung-Nam,Cho Jin-HO The Korean Society of Medical and Biological Engin 2005 의공학회지 Vol.26 No.6

Active contour models have been extensively used to segment, match, and track objects of interest in computer vision and image processing applications, particularly to locate object boundaries. With conventional methods an object boundary can be extracted by controlling the internal energy and external energy based on energy minimization. However, this still leaves a number of problems, such as initialization and poor convergence in concave regions. In particular, a contour is unable to enter a concave region based on the stretching and bending characteristic of the internal energy. Therefore, this study proposes a method that controls the internal energy by moving the local perpendicular bisector point of each control point on the contour, and determines the object boundary by minimizing the energy relative to the external energy. Convergence at a concave region can then be effectively implemented as regards the feature of interest using the internal energy, plus several objects can be detected using a multi-detection method based on the initial contour. The proposed method is compared with other conventional methods through objective validation and subjective consideration. As a result, it is anticipated that the proposed method can be efficiently applied to the detection of the pulmonary parenchyma region in medical images.

가상 물체의 기하학 정보 전달의 향상을 위한 휴대 단말 실감 인터페이스

한병길,김승찬,권동수,최태용,김휘수,경진호,김두형 제어·로봇·시스템학회 2020 제어·로봇·시스템학회 논문지 Vol.26 No.12

. In this paper, we propose a novel three-dimensional interaction system based on a shape-changeable mobile interface. We utilize multiple serially linked line segments to physically collocate virtual objects in real space. More specifically, the proposed system provides users with geometric information by physically enclosing the target virtual object with its outer shape. To this end, we further propose an algorithm that controls each joint of the system, such that the corresponding links are aligned with the virtual surface, based on an active-contour model. An experiment was conducted to verify the proposed interaction scheme, wherein geometric information was provided in the form of mechanical shape change of the interface. Our experimental results indicate that the proposed method is effective as a system for interacting with 3D virtual objects, provided that only a mobile interface is used.

Segmentation of Leaf Images Based on the Active Contours

Wu Peng,Li Wenlin,Song Wenlong 보안공학연구지원센터 2015 International Journal of u- and e- Service, Scienc Vol.8 No.6

Leaves contain important genetic information which can be used as a basis for the identification of plants. As a first step of modeling virtual three-dimensional plant, how to extract visual characteristic information form leaf images has great significance. We propose an optimized C-V model in this paper, which can detect objects in homogeneous regions of given leaf images and speed up running time. The new method combines local information with global information and optimizes the defect that SDF needs to be reconstructed partially so that the energy function is improved. Experimental results show that our algorithm can stop active contours on the correct boundary, get accurate image segmentation, and the speed is more than 1.5 times faster to C-V model.