• KIPS Transactions on Software and Data Engineering
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• v.3 no.5
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• pp.195-200
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• 2014

Typical methods of active contour model for object contour extraction are snake and level. Snake is usually faster than level set, but has limitation to compute topology of objects. Level set on the other hand is slower but good at it. In this paper, a new object contour extraction algorithm to use advantage of each is proposed. The algorithm is composed of two main steps. In the first step, snake is used to extract the rough contour and then in the second step, level set is applied to extract the complex contour exactly. 5 binary images and 2 natural images with different contours are simulated by a proposed algorithm. It is shown that speed is reduced and contour is better extracted.

• Journal of the Korean Institute of Intelligent Systems
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• v.18 no.2
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• pp.206-211
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• 2008

Trainee can enhance his perception of and interaction with the real world by displayed virtual objects in simulation using image processing technology. Therefore, it is essential for realistic simulation to determine the occlusion areas of the virtual object produces after registering real image and virtual object exactly. In this paper, we proposed the new method to solve occlusions which happens during virtual target moves according to the simulated route on real image using improved object contour extraction by neighboring edge search and picking algorithm. After we acquire the detailed contour of complex objects by proposed contour extraction algorithm, we extract the three dimensional information of the position happening occlusion by using MER for performance improvement. In the experiment, we compared proposed method with existed method and preyed the effectiveness in the environment which a partial occlusions happens.

• 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 the Institute of Electronics Engineers of Korea SP
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• v.48 no.1
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• pp.33-45
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• 2011

In this paper, we present a method for extracting video objects efficiently by using the modified graph cut algorithm based on contour information. First, we extract objects at the first frame by an automatic object extraction algorithm or the user interaction. To estimate the objects' contours at the current frame, motion information of objects' contour in the previous frame is analyzed. Block-based histogram back-projection is conducted along the estimated contour point. Each color model of objects and background can be generated from back-projection images. The probabilities of links between neighboring pixels are decided by the logarithmic based distance transform map obtained from the estimated contour image. Energy of the graph is defined by predefined color models and logarithmic distance transform map. Finally, the object is extracted by minimizing the energy. Experimental results of various test images show that our algorithm works more accurately than other methods.

• Journal of KIISE:Software and Applications
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• v.32 no.5
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• pp.357-365
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• 2005

We present a B-spline fitting method based on genetic algorithm for the extraction of object contours from the complex image sequence, where objects with similar shape and intensity are adjacent each other. The proposed algorithm solves common malfitting problem of the existing B-spline fitting methods including snakes. Classical snake algorithms have not been successful in such an image sequence due to the difficulty in initialization and existence of multiple extrema. We propose a B-spline fitting method using a genetic algorithm with a new initial population generation and fitting function, that are designed to take advantage of the contour of the previous slice. The test results show that the proposed method extracts contour of individual object successfully from the complex image sequence. We validate the algorithm by false-positive/negative errors and relative amounts of agreements.

• Journal of the Korea Society of Computer and Information
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• v.4 no.4
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• pp.77-85
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• 1999

In this paper. improved the formula of Kass. First of all, improved initial guess inside and outside of an object. So, prevent the of shrink, find more easily and faster the contour of object. Secondly, proposed the algorithm which moved to local minimum with the improvement of formula of the internal energy and $3{times}3$ matrix. Process the noise of local minimum with use of medial filtering. In third, process the phenomenon which edge points gather one point with imposing energy to the energy term. Improve the algorithm to find the contour precisely with the use of threshold. The result of these improvements, make an initial guess easily and find the contour of objects which have higher curvature. Improve the speed of process by reducing the repetition of feedback system.

• The KIPS Transactions:PartB
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• v.10B no.5
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• pp.527-534
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• 2003

An active contour model called snake is powerful tool for object contour extraction. But, conventional snakes require exhaustive computing time, sometimes can´t extract complex shape contours due to the properties of energy function, and are also heavily dependent on the position and the shape of an initial snake. To solving these problems, we propose in this paper an improved snake called "shaking snake", based on a greedy algorithm. A shaking snake consist of two steps. According to their appropriateness, we in the first step move each points directly to locations where contours are likely to be located. In the second step, we then align some snake points with a tolerable bound in order to prevent local minima. These processes shake the proposed snake. In the experimental results, we show the process of shaking the proposed shake and comparable performance with a greedy snake. The proposed snake can extract complex shape contours very accurately and run fast, approximately by the factor of five times, than a greedy snake.

• The Transactions of the Korea Information Processing Society
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• v.1 no.4
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• pp.551-558
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• 1994

This paper proposes the algorithm of the feature extraction, making polyline- shape according to extracted points and similarity test on the object represented by contour. The control points which can make approximate curve are extracted as features of the object. Experiments show that this algorithm is a effective method for identification between different shapes.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.37 no.6
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• pp.1-8
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• 2000

This paper describes an algorithm for semantic video object tracking using semi automatic method. In the semi automatic method, a user specifies an object of interest at the first frame and then the specified object is to be tracked in the remaining frames. The proposed algorithm consists of three steps: object boundary projection, uncertain area extraction, and boundary refinement. The object boundary is projected from the previous frame to the current frame using the motion estimation. And uncertain areas are extracted via two modules: Me error-test and color similarity test. Then, from extracted uncertain areas, the exact object boundary is obtained by boundary refinement. The simulation results show that the proposed video object extraction method provides efficient tracking results for various video sequences compared to the previous methods.

• Proceedings of the Korea Contents Association Conference
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• 2003.11a
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• pp.313-317
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• 2003

This paper suggested the initial edge map through the pre-processing of vague image before apply the GVF snake algorithm. The reason obtain for detail object outline and time efficiency GVF snake algorithm feasible extracted concave edge but mistake interested object edge for the around others. So it need to trim about the object around edges. The method is using Pixel morphological reconstruction, edge extraction mask and threshoding. The result, defend fallen local minimum edge energy and reduce iteration.