• The Transactions of the Korea Information Processing Society
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• v.6 no.7
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• pp.1962-1969
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• 1999

Active contours are deformable energy minimizing curves controlled by internal energy and external energy. The internal energy is constraint to preserve a smooth curve, and the external energy guides the curve towards image features. B-spline representation of active contours can be of great benefits in the segmentation and description whose shape is characterized by its defining polygon or control points. Menet et al proposed B-spline representation of active contours based on dynamic programming. The method is simple and efficient by comparing over finite difference method.

• 제어로봇시스템학회:학술대회논문집
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• 1996.10b
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• pp.89-92
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• 1996

In this paper, we developed visual tracking algorithm using double active bar. The active bar model to represent the object can reduce the search space of energy surface and better performance than those of snake model. However, the contour will not find global equilibrium when driving force caused by image may be weak. To overcome this problem. Double active bar is proposed for finding the global minimum point without any dependence on initialization. To achieve the goal, an deformable model with two initial contours in attempted to search for a global minimum within two specific initial contours. This approach improve the performance of finding the contour of target. To evaluate the performance, some experiments are executed. We can achieved the good result for tracking a object on noisy image.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.5
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• pp.1840-1855
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• 2015

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.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.42 no.1
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• pp.79-87
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• 2005

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.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.7 no.11
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• pp.2839-2852
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• 2013

Context-awareness is an essential part of ubiquitous computing, and over the past decade video based activity recognition (VAR) has emerged as an important component to identify user's context for automatic service delivery in context-aware applications. The accuracy of VAR significantly depends on the performance of the employed human body segmentation algorithm. Previous human body segmentation algorithms often engage modeling of the human body that normally requires bulky amount of training data and cannot competently handle changes over time. Recently, active contours have emerged as a successful segmentation technique in still images. In this paper, an active contour model with the integration of Chan Vese (CV) energy and Bhattacharya distance functions are adapted for automatic human body segmentation using depth cameras for VAR. The proposed technique not only outperforms existing segmentation methods in normal scenarios but it is also more robust to noise. Moreover, it is unsupervised, i.e., no prior human body model is needed. The performance of the proposed segmentation technique is compared against conventional CV Active Contour (AC) model using a depth-camera and obtained much better performance over it.

• 제어로봇시스템학회:학술대회논문집
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• 2004.08a
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• pp.312-316
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• 2004

The paper presents a technique to identify the boundary of the optic disc in infant retinal digital images using an approach based on active contours (snakes). The technique can be used to be develop a automate system in order to help the ophthalmologist's diagnosis the retinopathy of prematurity (ROP) disease which may occurred on preterm infant,. The optic disc detection is one of the fundamental step which could help to create an automate diagnose system for the doctors we use a new kind of active contour (snake) method has been developed by Chenyang et. al. [1], based on a new type of external force field, called gradient vector flow, or GVF. GVF is computed as a diffusion of the gradient vectors of a gray-level or binary edge map derived from the image. The testing results on a set of infant retinal ROP images verify the effectiveness of the proposed methods. We show that GVF has a large capture range and it's able to move snakes into boundary concavities of optic disc and finally the optic disk boundary was determined.

• Journal of Information Processing Systems
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• v.7 no.3
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• pp.519-530
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• 2011

A Snake is an active contour for representing object contours. Traditional snake algorithms are often used to represent the contour of a single object. However, if there is more than one object in the image, the snake model must be adaptive to determine the corresponding contour of each object. Also, the previous initialized snake contours risk getting the wrong results when tracking multiple objects in successive frames due to the weak topology changes. To overcome this problem, in this paper, we present a new snake method for efficiently tracking contours of multiple objects. Our proposed algorithm can provide a straightforward approach for snake contour rapid splitting and connection, which usually cannot be gracefully handled by traditional snakes. Experimental results of various test sequence images with multiple objects have shown good performance, which proves that the proposed method is both effective and accurate.

• Proceedings of the Korea Multimedia Society Conference
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• 2002.05c
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• pp.195-199
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• 2002

본 논문에서는 주어진 입력 이미지에서 얼굴 영역을 검출하기 위한 액티브 컨투어 모델(active contour models)을 제안한다. 제안한 모델은 스킨 칼라 모델(skin color model)에 의해 표현되는 사람 얼굴의 칼라 정보를 이용한다. 본 논문에서는 첨점(cusps), 모서리 (corners), 그리고 자동 위상 변화(automatic topological changes)를 고려한 레벨 셋 메소드(level set method)를 사용하여 액티브 컨투어를 진화시킨다. 실험 결과는 제안한 방법이 얼굴 영역 검출에 효과가 있음을 보여준다.

• The KIPS Transactions:PartB
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• v.17B no.5
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• pp.375-380
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• 2010

Most of previous algorithms of object boundary extraction have been studied for extracting the boundary of single object. However, multiple objects are much common in the real image. The proposed algorithm of extracting the boundary of each of multiple objects has two steps. In the first step, we propose the fast method using the outer and inner products; the initial contour including multiple objects is split and connected and each of new contours includes only one object. In the second step, an improved active contour model is studied to extract the boundary of each object included each of contours. Experimental results with various test images have shown that our algorithm produces much better results than the previous algorithms.

• Journal of Digital Convergence
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• v.10 no.3
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• pp.167-172
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• 2012

This paper presents the technique separating objects on the single frame image from the background using region segmentation technique based on active contour. Active contour is to extract contours of objects from the image, which is set to have multi-search starting point to extract each objects contours for multi-object segmentation. Initial rough object segments are generated from binary-coded image using object specific contour information, and then the hole filling is performed to compensate internal segmentation caused by the change of inner object hole area and pixels. This procedure complements the problems caused by the noise from the region segmentation and the errors of segmentation near by the contour. The proposed method and conventional method is compared to verify the superiority of the proposed method.