• Journal of the Korea Computer Graphics Society
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• v.9 no.3
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• pp.1-5
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• 2003

3차원 메쉬에서 특징을 추출하는 것은 메쉬 에디팅이나 메쉬 모핑 등의 여러 가지 메쉬 처리에 있어서 중요한 일이다. 특징을 추출하는 방법 중에서 사용자가 지정한 부근의 특징을 자동적으로 찾아주는 방법은 이미지 처리 분야에서는 오래 전부터 사용되어 왔는데 이미지 스네이크 알고리즘이 그것이다. 최근에는 그러한 이미지 스네이크 알고리즘이 3차원 메쉬에 적용되어 기하학적인 스네이크 알고리즘으로 탄생하였다. 본 논문은 기하학적 스네이크의 새로운 알고리즘을 제시하고, 찾고자 하는 특징의 모양에 따라 스네이크 곡선의 위상이 자유롭게 변화하는 기하학적 스네이크 모델을 제안한다. 본 논문에 사용된 알고리즘은 이미지 스네이크 알고리즘의 동적 프로그래밍 방법을 3차원 메쉬에 응용한 것으로 스네이크 포인트들이 메쉬의 에지를 따라 3차원 상에서 직접 이동을 하면서 에너지가 최소가 되는 지점을 찾아 가는 방식이다. 스네이크 곡선은 메쉬상의 이웃한 정점들의 순차적인 연결선으로 이루어지며 찾고자 하는 특징의 모양과 크기에 따라 스네이크 포인트의 개수가 자동으로 조절된다. 또한 주변의 다른 스네이크 포인트와 만났을 때 합쳐지거나 반대로 여러 스네이크 곡선으로 나뉘어 질 수 있다.

• KIPS Transactions on Software and Data Engineering
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• v.4 no.8
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• pp.309-314
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• 2015

One of typical methods for extracting facial features from face images may be snake. Although snake is simple and fast, performance is very much affected by the initial contour and the shape of object to be extracted. In this paper, the enhanced snake is proposed to extract better facial features from 6 lip and mouth images as snake point is added to the midpoint of snake segment. It is shown that RSD of the proposed method is about 2.8% to 5.8% less than that of Greedy snake about 6 test face images. Since lesser RSD is especially obtained for contours with highly concavity, the contour is more accurately extracted.

• Proceedings of the Korean Information Science Society Conference
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• 2003.04c
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• pp.196-198
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• 2003

본 논문에서는 스네이크 모델의 에너지 최소화 알고리즘을 개선하여 속도와 정확도에 대한 문제를 해결한다. 개선된 알고리즘은 스네이크를 이루는 정점들의 적합성에 따라 탐색 윈도우를 가변적으로 확장시킴으로써 빠르고 정확하게 윤곽선을 추출한다. 또한 정점의 정렬과정을 통해 정점이 지역적 최소점에 빠지는 것을 방지하며 스네이크의 연속성과 완만성을 보존한다.

• The KIPS Transactions:PartB
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• v.19B no.4
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• pp.221-224
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• 2012

The most famous algorithm of splitting and connecting Snake for extracting the boundary of multiple objects is the nearest method using the distance between snake points. It often can't split and connect Snake due to object topology. In this paper, its problem was discussed experimentally. The new algorithm using vector between Snake segment is proposed in order to split and connect Snake with complicated topology of objects. It is shown by experiment of two test images with 3 and 5 objects that the proposed one works better than the nearest one.

• The KIPS Transactions:PartB
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• v.12B no.7 s.103
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• pp.767-774
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• 2005

In this paper, we present a snake-based scheme for tracking object contour using disparity information taken from a stereo image sequence with cluttered background. The proposed method is composed of two steps. First, 3-D motion of object is estimated and candidate snake points are selected in disparity space. Second, object contour is extracted by using a modified snake algorithm with disparity information. The proposed algorithm can successfully extract the concave contour of objects and track the object contour in complex image. Performance of the proposed algorithm has been verified by simulation.

• Journal of the Korea Computer Graphics Society
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• v.7 no.3
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• pp.9-18
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• 2001

Feature detection is important in various mesh processing techniques, such as mesh editing, mesh morphing, mesh compression, and mesh signal processing. In this paper, we propose a geometric snake as an interactive tool for feature detection on a 3D triangular mesh. A geometric snake is an extension of an image snake, which is an active contour model that slithers from its initial position specified by the user to a nearby feature while minimizing an energy functional. To constrain the movement of a geometric snake onto the surface of a mesh, we use the parameterization of the surrounding region of a geometric snake. Although the definition of a feature may vary among applications, we use the normal changes of faces to detect features on a mesh.

• The KIPS Transactions:PartB
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• v.13B no.4 s.107
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• pp.361-368
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• 2006

In this paper we present a snake-based scheme for efficiently detecting contours of objects with boundary concavities. The proposed method is composed of two steps. First, the object's boundary is detected using the proposed snake model. Second, snake points are optimized by inserting new points and deleting unnecessary points to better describe the object's boundary. The proposed algorithm can successfully extract objects with boundary concavities. Experimental results have shown that our algorithm produces more accurate segmentation results than the conventional algorithm.

• Journal of KIISE:Software and Applications
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• v.37 no.11
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• pp.866-870
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• 2010

This paper presents an improved Snake algorithm that contains additional energy term related to adjacent edges. The suggested algorithm represents the distance between an adjacent edge and the current cell as energy, and extracts object contours more effectively by including the energy tenn to the whole energy function. The adjacent edge-based snake algorithm not only make it possible to detect object boundaries which are concave, but also can detect the boundaries of complex objects without weight adjustment. Experimental results show that the proposed method extracts object boundaries more accurately than other existing methods without loss of speed.

• The KIPS Transactions:PartB
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• v.13B no.3 s.106
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• pp.239-244
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• 2006

In this paper, we present a snake-based scheme for contour tracking of objects in stereo image sequences. We address the problem by managing the insertion of new points and deletion of unnecessary points to better describe and track the object's boundary. In particular, our method uses more points in highly curved parts of the contour, and fewer points in less curved parts. The proposed algorithm can successfully define the contour of the object, and can track the contour in complex images. Furthermore, we tested our algorithm in the presence of partial object occlusion. Performance of the proposed algorithm has been verified by simulation.

• Journal of the Korea Society of Computer and Information
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• v.14 no.10
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• pp.83-92
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• 2009

In this paper, an extended snake algorithm using color variance energy is proposed for segmenting an interest object in color image. General snake algorithm makes use of energy in image to segment images into a interesting area and background. There are many kinds of energy that can be used by the snake algorithm. The efficiency of the snake algorithm is depend on what kind of energy is used. A general snake algorithm based on active contour model uses the intensity value as an image energy that can be implemented and analyzed easily. But it is sensitive to noises because the image gradient uses a differential operator to get its image energy. And it is difficult for the general snake algorithm to be applied on the complex image background. Therefore, the proposed snake algorithm efficiently segment an interest object on the color image by adding a color variance of the segmented area to the image energy. This paper executed various experiments to segment an interest object on color images with simple or complex background for verifying the performance of the proposed extended snake algorithm. It shows improved accuracy performance about 12.42 %.