• Journal of the Institute of Electronics Engineers of Korea SP
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• v.48 no.6
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• pp.105-114
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• 2011

The snake algorithm is widely adopted to track objects by extracting the active contour of the object from background. However, it fails to track the target converging to the background if there exists background whose gradient is greater than that of the pixels on the contour. Also, the contour may shrink when the target moves fast and the snake algorithm misses the boundary of the object in its searching window. To alleviate these problems, we propose an improved algorithm that can track object contour more robustly. Firstly, we propose two external energy functions, the edge energy and the contrast energy. One is designed to give more weight to the gradient on the boundary and the other to reflect the contrast difference between the object and background. Secondly, by computing the motion vector of the contour from the difference of the two consecutive frames, we can move the snake pointers of the previous frame near the region where the object boundary is probable at the current frame. Computer experiments show that the proposed method is more robust to the complicated background than the previously known methods and can track the object with fast movement.

• Journal of information and communication convergence engineering
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• v.7 no.1
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• pp.41-45
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• 2009

In mobile GIS environments, a client needs to receive the isogram data with a topographical map from a server, if the mobile client wants to display map with elevation information. Because the elevation data size is normally quite large, the client will suffer some problems to receive the elevation data from server. The main reason is a resource limitation of the mobile devices. To overcome these problems, this paper proposes new data structures and algorithms. They are designed for efficient transmission of contour data to a client. Because of the contour data are generated as a vector style from elevation information stored at a server, the proposed algorithms are focused to minimize the transmission data volume and time.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1998.10a
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• pp.255-262
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• 1998

This paper presents a translation, rotation and scale invariant methodology for the recognition of closed planar shape images using the bispectrum of a contour sequence and the learning vector quantization(LVQ) neural classifier. The contour sequences obtained from the closed planar images represent the Euclidean distance between the centroid and all boundary pixels of the shape, and are related to the overall shape of the images. The higher order spectra based on third order cumulants is applied to tihs contour sample to extract fifteen bispectral feature vectors for each planar image. There feature vector, which are invariant to shape translation, rotation and scale transformation, can be used to represent two0dimensional planar images and are fed into a neural network classifier. The LVQ architecture is chosen as a neural classifier because the network is easy and fast to train, the structure is relatively simple. The experimental recognition processes with eight different hapes of aircraft images are presented to illustrate the high performance of this proposed method even the target images are significantly corrupted by noise.

• Journal of Biomedical Engineering Research
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• v.23 no.2
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• pp.101-107
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• 2002

This paper presents a segmentation algorithm to extract endocardial contour and epicardial contour of left ventricle in MR Cardiac images. The algorithm is based on a generalized gradient vector flow(GGVF) snake and a prediction of initial contour(PIC). Especially. the proposed algorithm uses physical characteristics of endocardial and epicardial contours, cross profile correlation matching(CPCM), and a mixed interpolation model. In the experiment, the proposed method is applied to short axis MR cardiac image set, which are obtained by Siemens, Medinus, and GE MRI Systems. The experimental results show that the proposed algorithm can extract acceptable epicardial and endocardial walls. We calculate quantitative parameters from the segmented results, which are displayed graphically. The segmented left vents role is visualized volumetrically by surface rendering. The proposed algorithm is implemented on Windows environment using Visual C ++.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1997.10a
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• pp.341-344
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• 1997

The higher precision in manufacturing field is demanded, the more accurate servo controller is needed. To achieve the high precision, Koren proposed the cross-coupled control (CCC) method. The objective of the CCC is reducing the contour error rather than decreasing the individual axial error. The performance of CCC depends on the contour error model. In this paper we propose a new contour error model which utilizes contour error vector based on parametric curve interpolator. The experimental results show that the new CCC is more accurate than the variable-gain CCC during free-form curve motion.

• Journal of Information Processing Systems
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• v.9 no.1
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• pp.103-116
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• 2013

Prostate cancer is one of the most frequent cancers in men and is a major cause of mortality in the most of countries. In many diagnostic and treatment procedures for prostate disease accurate detection of prostate boundaries in transrectal ultrasound(TRUS) images is required. This is a challenging and difficult task due to weak prostate boundaries, speckle noise and the short range of gray levels. In this paper a method for automatic prostate segmentation in TRUS images using Gabor feature extraction and snake-like contour is presented. This method involves preprocessing, extracting Gabor feature, training, and prostate segmentation. The speckle reduction for preprocessing step has been achieved by using stick filter and top-hat transform has been implemented for smoothing the contour. A Gabor filter bank for extraction of rotation-invariant texture features has been implemented. A support vector machine(SVM) for training step has been used to get each feature of prostate and nonprostate. Finally, the boundary of prostate is extracted by the snake-like contour algorithm. A number of experiments are conducted to validate this method and results showed that this new algorithm extracted the prostate boundary with less than 10.2% of the accuracy which is relative to boundary provided manually by experts.

• ETRI Journal
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• v.38 no.1
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• pp.195-205
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• 2016

This paper presents a novel follicular unit (FU) classification method based on an angle variation of a boundary vector according to the number of hairs in several FU images. The recently developed robotic FU harvest system, ARTAS, classifies through digital imaging the FU type based on the number of hairs with defects in the contour and outline profile of the FU of interest. However, this method has a drawback in that the FU classification is inaccurate because it causes unintended defects in the outline profile of the FU. To overcome this drawback, the proposed method classifies the FU's type by the number of variation points that are calculated using an angle variation a boundary vector. The experimental results show that the proposed method is robust and accurate for various FU shapes, compared to the contour-outline profile FU classification method of the ARTAS system.

• Journal of the Korea Computer Graphics Society
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• v.23 no.5
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• pp.29-38
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• 2017

We propose a method for tree-inspired chair modeling that can generate a tree-branch pattern in the skeleton of an arbitrary chair shape. Unlike existing methods that merge multiple-input models, the proposed method requires only one mesh as input, namely the contour mesh of the user's desired part, to model the chair with a branch pattern generated by tree-growth simulation. We propose a new method for the efficient extraction of the contour-mesh region in the tree-branch pattern. First, we extract the contour mesh based on the face area of the input mesh. We then use the front and back mesh information to generate a skeleton mesh that reconstructs the connection information. In addition, to obtain the tree-branch pattern matching the shape of the input model, we propose a three-way tree-growth simulation method that considers the tangent vector of the shape surface. The proposed method reveals a new type of furniture modeling by using an existing furniture model and simple parameter values to model tree branches shaped appropriately for the input model skeleton. Our experiments demonstrate the performance and effectiveness of the proposed method.

• Advances in aircraft and spacecraft science
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• v.1 no.4
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• pp.399-408
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• 2014

The flowfields inside a contour and a conical nozzle exhausting into a straight cylindrical supersonic diffuser are computed by solving numerically axisymmetric turbulent compressible Navier-Stokes equations for stagnation to ambient pressure ratios in the range 20 to 34. The diffuser inlet-to-nozzle throat area ratio and exit-to-throat area ratio are 21.77, and length-to-diameter ratio of the diffuser is 5. The flow characteristics of the conical and contour nozzle are compared with the help of velocity vector and Mach contour plots. The variations of Mach number along the centre line and wall of the conical nozzle, contour nozzle and the straight supersonic diffuser indicate the location of the shock and flow characteristics. The main aim of the present analysis is to delineate the flowfields of conical and contour nozzles operating under identical conditions and exhausting into a straight cylindrical supersonic diffuser.

• Journal of Institute of Control, Robotics and Systems
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• v.16 no.12
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• pp.1226-1232
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• 2010

A door can serve as a feature for place classification and localization for navigation of a mobile robot in indoor environments. This paper proposes a door detection method based on the recognition of various door handles using the general Hough transform (GHT) and support vector machine (SVM). The contour and color histogram of a door handle extracted from the database are used in GHT and SVM, respectively. The door recognition scheme consists of four steps. The first step determines the region of interest (ROI) images defined by the color information and the environment around the door handle for stable recognition. In the second step, the door handle is recognized using the GHT method from the ROI image and the image patches are extracted from the position of the recognized door handle. In the third step, the extracted patch is classified whether it is the image patch of a door handle or not using the SVM classifier. The door position is probabilistically determined by the recognized door handle. Experimental results show that the proposed method can recognize various door handles and detect doors in a robust manner.