• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2007.10a
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• pp.277-280
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• 2007

A moving object tracking algorithm for image searching based on specific color detection is proposed in this paper. That is preprocessed for a luminance variation and noise cancellation to be robust system. The motion tracking is used the difference between input image and reference image in R, G, B each channels for a moving image. The proposed method is enhanced to 15% fast in comparison with the contour tracking method and the matching method, and stable.

• Journal of information and communication convergence engineering
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• v.6 no.2
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• pp.213-217
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• 2008

This paper proposes a novel method for the recognition of passports based on the CDM(Conditional Dilation Morphology) masking and the ART2-based RBF neural networks. For the extraction of individual codes for recognizing, this paper targets code sequence blocks including individual codes by applying Sobel masking, horizontal smearing and a contour tracking algorithm on the passport image. Individual codes are recovered and extracted from the binarized areas by applying CDM masking and vertical smearing. This paper also proposes an ART2-based hybrid network that adapts the ART2 network for the middle layer. This network is applied to the recognition of individual codes. The experiment results showed that the proposed method has superior in performance in the recognition of passport.

• Journal of information and communication convergence engineering
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• v.7 no.2
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• pp.131-134
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• 2009

The red-eye effect in photography occurs when using a photographic flash very close to the camera lens, in ambient low light due to in experience. Once occurred, the photographer needs to remove it with image tool that requires time consuming, skillful process. In this paper, we propose a new method to extract and remove such red-eye area automatically. Our method starts with transforming ROB space to YCbCr and HSI space and it extracts the face area by using skin color information. The target red-eye area is then extracted by applying 8-direction contour tracking algorithm and removed. The experiment shows our method's effectiveness.

• The Journal of the Korea institute of electronic communication sciences
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• v.6 no.2
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• pp.243-248
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• 2011

In this paper, we propose a new method to extract palm lines and read it with simple techniques from one photo. We use morphological information and 8-directional contour tracking algorithm. From the digitalized image, we transform original RGB information to YCbCr color model which is less sensitive to the brightness information. The palm region is extracted by simple threshold as Y:65~255, Cb:25~255, Cr:130~255 of skin color. Noise removal process is then followed with morphological information of the palm such that the palm area has more than quarter of the pixels and the rate of width vs height is more than 2:1 and 8-directional contour tracking algorithm. Then, the stretching algorithm and Sobel mask are applied to extract edges. Another morphological information that the meaningful edges(palm lines) have between 10 and 20 pixels is used to exclude noise edges and boundary lines of the hand from block binarized image. Main palm lines are extracted then by labeling method. This algorithm is quite effective even reading the palm from a photographed by a mobile phone, which suggests that this method could be used in various applications.

• Journal of Korea Society of Industrial Information Systems
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• v.12 no.5
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• pp.39-46
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• 2007

Research on human identification in ubiquitous environment has recently attracted a lot of attention. As one of those research, gait recognition is an efficient method of human identification using physical features of a walking person at a distance. In this paper, we present a human shape extraction and tracking for gait recognition using geodesic active contour models(GACMs) combined with mean shift algorithm The active contour models (ACMs) are very effective to deal with the non-rigid object because of its elastic property. However, they have the limitation that their performance is mainly dependent on the initial curve. To overcome this problem, we combine the mean shift algorithm with the traditional GACMs. The main idea is very simple. Before evolving using level set method, the initial curve in each frame is re-localized near the human region and is resized enough to include the targe region. This mechanism allows for reducing the number of iterations and for handling the large object motion. The proposed system is composed of human region detection and human shape tracking modules. In the human region detection module, the silhouette of a walking person is extracted by background subtraction and morphologic operation. Then human shape are correctly obtained by the GACMs with mean shift algorithm. In experimental results, the proposed method show that it is extracted and tracked efficiently accurate shape for gait recognition.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.42 no.3 s.303
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• pp.15-22
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• 2005

This paper proposed a object tracking algorithm which can track contour of fast moving object through motion estimation. Since the proposed tracking algorithm is based on the radial representation, the motion estimation of object can be accomplished at the center of object with the low computation complexity. The motion estimation of object makes it possible to track object which move fast more than distance from center point to contour point for each frame. In addition, by introducing both gradient image and difference image into energy functions in the process of energy convergence, object tracking is more robust to the complex background. The results of experiment show that the proposed algorithm can track fast moving object in real-time and is robust under the complex background.

• The Journal of the Korea Contents Association
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• v.11 no.10
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• pp.11-18
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• 2011

In the field of image processing to track the fingertip much research has been done. The most common way to calculate the fingertip first, to extract color information. Then, it uses Blob Coloring algorithms which are expressed in blob functions the skin contour and calculates. The algorithm from contour decides the highest location with the fingertip. But this method when measuring it location from the finger condition which bents is not the actual fingertip and has the problem which detects the location which goes wrong. This paper proposes the color space correction algorithm to tracks the fingertip which bents. The method which proposes when tracking the fingertip from the finger condition which bents solves the problem which measures the location which goes wrong. Aim of this paper in compliance with the propensity of the users forecasts a problem in advance and corrects with improvement at the time of height boil an efficiency. Ultimately, this paper suggests empirical application to verify the adequacy and the validity with the proposed method. Accordingly, the satisfaction and the quality of services will be improved the image recognition.

• Transactions of the Korean Society of Mechanical Engineers
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• v.19 no.5
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• pp.1220-1228
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• 1995

In this paper, we consider the problems of tracking an object in a real image. In evaluating these problems, we explore a new technique based on an active contour model commonly called a snake model, and propose the active bar models to represent target. Using this model, we simplified the target welection problems, reduced the search space of energy surface, and obtained the better performances than those of snake model. This approach improves the numerical stability and the tendency for points to bunch up and speed up the computational efficiency. Representing the object by active bar, we can easily obtain the zeroth, the first, and the second moment and it facilitates the target tracking. Finally, we present the good result for the visual tracking problem.

• Journal of the Korean Institute of Telematics and Electronics S
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• v.36S no.11
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• pp.79-88
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• 1999

In this paper we address the tracking method which tracks only target object in image sequence including moving object. We use a contour tracking algorithm based on intensity and motion boundaries. The motion of the moving object contour in the image is assumed to be well describable by an affine motion model with a translation, a change in scale and a rotation. The moving object contour is represented by B-spline, the position and motion of which is estimated along the image sequence. we use pattern recognition to identify target object. In order to use linear Kalman Filters we decompose the estimation process into two filters. One is estimating the affine motion parameters and the other the shape of moving object contour. In some experiments with dial plate we show that this method enables us to obtain the robust motion estimates and tracking trajectories even in case of including obstructive object.

• Journal of the Korean Institute of Intelligent Systems
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• v.14 no.3
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• pp.349-356
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• 2004

The automatic recognition of transport containers using image processing is very hard because of the irregular size and position of identifiers, diverse colors of background and identifiers, and the impaired shapes of identifiers caused by container damages and the bent surface of container, etc. In this paper we propose and evaluate a novel recognition algorithm for container identifiers that effectively overcomes these difficulties and recognizes identifiers from container images captured in various environments. The proposed algorithm, first, extracts the area containing only the identifiers from container images by using CANNY masking and bi-directional histogram method. The extracted identifier area is binarized by the fuzzy binarization method newly proposed in this paper. Then a contour tracking method is applied to the binarized area in order to extract the container identifiers which are the target for recognition. In this paper we also propose and apply a novel ART2-based hybrid network for recognition of container identifiers. The results of experiment for performance evaluation on the real container images showed that the proposed algorithm performs better for extraction and recognition of container identifiers compared to conventional algorithms.