• Proceedings of the Korea Contents Association Conference
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• 2005.11a
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• pp.633-638
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• 2005

The extraction of a face is a very important part for human interface, biometrics and security. In this paper, we applies DCM(Dilation of Color and Motion) filter and Active Contour Models to extract facial outline. First, DCM filter is made by applying morphology dilation to the combination of facial color image and differential image applied by dilation previously. This filter is used to remove complex background and to detect facial outline. Because Active Contour Models receive a large effect according to initial curves, we calculate rotational degree using geometric ratio of face, eyes and mouth. We use edgeness and intensity as an image energy, in order to extract outline in the area of weak edge. We acquire various head-pose images with both eyes from five persons in inner space with complex background. As an experimental result with total 125 images gathered by 25 per person, it shows that average extraction rate of facial outline is 98.1% and average processing time is 0.2sec.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.40 no.10
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• pp.2090-2101
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• 2015

We propose a robust tracking method that combines the merits of ACM(active contour model) and the color-based PF(particle filter), effectively. In the proposed method, PF and ACM track the color distribution and the contour of the target, respectively, and Decision part merges the estimate results from the two trackers to determine the position and scale of the target and to update the target model. By controlling the internal energy of ACM based on the estimate of the position and scale from PF tracker, we can prevent the snake pointers from falsely converging to the background clutters. We appled the proposed method to track the head of person in video and have conducted computer experiments to analyze the errors of the estimated position and scale.

• Journal of KIISE:Software and Applications
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• v.33 no.1
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• pp.95-104
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• 2006

The Gradient Vector Flow (GVF) snake or active contour model offers the best performance for image segmentation. However, there are problems in classical snake models such as the limited capture range and the slow progress into concavity. This paper presents a new method for enhancing the performance of the GVF snake model by extending the external force fields from the neighboring fields and using a modified smoothing method to regularize them. The results on a simulated U-shaped image showed that the proposed method has larger capture range and makes it possible for the contour to progress into concavity more quickly compared with the conventional GVF snake model.

• 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.

• Proceedings of the KOSOMBE Conference
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• v.1997 no.11
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• pp.541-545
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• 1997

This paper presents a modified geometric active contour model or edge detection and segmentation of computed tomography(CT) scan images. The method is based on the level setup approach developed by Osher and Sethian and the modeling of propagation fronts with curvature dependent speeds by Malladi. Based on above algorithms, the geometric active contour is obtained through a particular level set of hypersurface lowing along its gradient force and curvature force. This technique retains the attractive feature which is topological and geometric flexibility of the contour in recovering objects with complex shapes and unknown topologies. But there are limitations in this algorithm which are being not able to separate the object with weak difference from neighbor object. So we use speed limitation filter to overcome those problems. We apply a 2D model to various synthetic cases and the three cases of real CT scan images in order to segment objects with complicated shapes and topologies. From the results, the presented model confirms that it attracts very naturally and efficiently to the desired feature of CT scan images.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.47 no.1
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• pp.1-8
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• 2010

Some methods employing the Active Contour Model have been widely used to extract face contour. Their performance, however, depends on the initial position of the model and the coefficients of the energy function which should be reconsidered whenever illumination and environmental condition of an input image is changed. Additionally, the number of points in the contour model should increase drastically in order to extract a fine contour. In this paper, we thus propose a novel approach which extracts face contour by segmenting the face region with threshold values obtained by a histogram analysis technique in the separated region of input image. The proposed method shows good performance under various illumination and environmental condition since it extracts face contour by considering the characteristics of the input image.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.43 no.4 s.310
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• pp.58-66
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• 2006

In this paper, we apply DCM(Dilation of Color and Motion information) mask and Active Contour Models(Snakes) to extract facial outline in moving pictures with complex background. First, we propose DCM mask which is made by applying morphology dilation and AND operation to combine facial color and motion information, and use this mask to detect facial region without complex background and to remove noise in image energy. Also, initial curves are automatically set according to rotational degree estimated with geometric ratio of facial elements to overcome the demerit of Active Contour Models which is sensitive to initial curves. And edge intensity and brightness are both used as image energy of snakes to extract contour at parts with weak edges. For experiments, we acquired total 480 frames with various head-poses of sixteen persons with both eyes shown by taking pictures in inner space and also by capturing broadcasting images. As a result, it showed that more elaborate facial contour is extracted at average processing time of 0.28 seconds when using interpolated initial curves according to facial rotation degree and using combined image energy of edge intensity and brightness.

• Journal of the Institute of Convergence Signal Processing
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• v.21 no.4
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• pp.209-215
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• 2020

In this paper, we propose an algorithm for segmenting weld bead images using edge enhancement and active contours. In the proposed method, high-frequency filtering and contrast improvement are performed for edge enhancement, and then, by applying the active contour method, only the weld bead region can be obtained. The proposed algorithm detects an edge through high-frequency filtering and reinforces the detected edge by using contrast enhancement. After the edge information is improved in this way, the weld bead area can be extracted by applying the active contour method. The proposed algorithm shows better performance than the existing methods for segmenting the weld bead in the image. For the objective reliability of the proposed algorithm, it was compared with the existing high pass filtering methods, and it was confirmed that the welding bead segmentation of the proposed method is excellent. The proposed method can be usefully used in evaluating the quality of the weld bead through an additional procedure for the segmented weld bead.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.28 no.11C
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• pp.1088-1097
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• 2003

In this paper, we propose a method that improves the snakes algorithm well known as previously active contour model. Generally, the previous snakes algorithm applied to the 2-D images doesn't get the good results due to the influences about other objects adjacent to contour of object to be extracted. Users directly set the initial snakes points near to the contour of the object to get better results. In this paper, using the disparity information of the stereo images, a new algorithm of the object segmentation is proposed to reduce the influences adjacent to the contour of object. Users can establish initial snakes points automatically from the setting of the interested regions.

• Nuclear Engineering and Technology
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• v.39 no.4
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• pp.313-326
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• 2007

In this paper, an algorithm to reconstruct two orthogonal images into a three-dimensional image is developed in order to measure the bubble size and volume in a two-phase boiling flow. The central-active contour model originally proposed by P. $Szczypi\'{n}ski$ and P. Strumillo is modified to reduce the dependence on the initial reference point and to increase the contour stability. The modified model is then applied to the algorithm to extract the object boundary. This improved central contour model could be applied to obscure objects using a variable threshold value. The extracted boundaries from each image are merged into a three-dimensional image through the developed algorithm. It is shown that the object reconstructed using the developed algorithm is very similar or identical to the real object. Various values such as volume and surface area are calculated for the reconstructed images and the developed algorithm is qualitatively verified using real images from rubber clay experiments and quantitatively verified by simulation using imaginary images. Finally, the developed algorithm is applied to measure the size and volume of vapor bubbles condensing in a subcooled boiling flow.