• Journal of Advanced Navigation Technology
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• v.15 no.1
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• pp.140-146
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• 2011

In this paper, we propose a fast object classification method based on texture and color information for video surveillance. We take the advantage of local patches by extracting SURF and color histogram from images. SURF gives intensity content information and color information strengthens distinctiveness by providing links to patch content. We achieve the advantages of fast computation of SURF as well as color cues of objects. We use Bag of Word models to generate global descriptors of a region of interest (ROI) or an image using the local features, and Na$\ddot{i}$ve Bayes model for classifying the global descriptor. In this paper, we also investigate discriminative descriptor named Scale Invariant Feature Transform (SIFT). Our experiment result for 4 classes of the objects shows 95.75% of classification rate.

• Journal of the Korea Society of Computer and Information
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• v.15 no.3
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• pp.27-35
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• 2010

Accurately extracting skin color regions is very important in various areas such as face recognition and tracking, facial expression recognition, adult image identification, health-care, and so forth. In this paper, we evaluate the performances of several skin color detection algorithms in indoor environments by changing the distance between the camera and the object as well as the background colors of the object. The distance is from 60cm to 120cm and the background colors are white, black, orange, pink, and yellow, respectively. The algorithms that we use for the performance evaluation are Peer algorithm, NNYUV, NNHSV, LutYUV, and Kimset algorithm. The experimental results show that NNHSV, NNYUV and LutYUV algorithm are stable, but the other algorithms are somewhat sensitive to the changes of backgrounds. As a result, we expect that the comparative experimental results of this paper will be used very effectively when developing a new skin color extraction algorithm which are very robust to dynamic real environments.

• The KIPS Transactions:PartB
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• v.11B no.5
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• pp.581-586
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• 2004

In this paper, we proposed a content-based image retrieval using a color feature for central region and its optimized comparing bin method. Human's visual characteristic is influenced by existent of central object. So we supposed that object is centrally located in image and then we extract color feature at central region. When the background of image is simple, the retrieval result can be bad affected by major color of background. Our method overcome this drawback as a result of the human visual characteristic. After we transform Image into HSV color space, we extract color feature from the quantized image with 16 level. The experimental results showed that the method using the eight high rank bin is better than using the 16 bin The case which extracts the feature with image's central region was superior compare with the case which extracts the feature with the whole image about 5%.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.2 no.1
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• pp.23-28
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• 2009

Recently, the research which concerns the object class recognition has been done. Although an object tracking based on most of histograms employs a colored model to improve robustness, the system is not reliable enough yet. In this paper, we presents a method to express and track an object by using the histograms which are composed with visual features through succesive frames. The experimental results shows that this method is reliable to track a car within 80m distance from camera.

• International Journal of Internet, Broadcasting and Communication
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• v.7 no.2
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• pp.1-8
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• 2015

Tracking of an object in 3D space performed in real-time is a significant task in different domains from autonomous robots to smart vehicles. In traditional methods, specific data acquisition equipments such as radars, lasers etc, are used. Contemporary computer technology development accelerates image processing, and it results in three-dimensional stereo vision to be used for localizing and object tracking in space. This paper describes a system for tracking three dimensional motion of an object using color information in real time. We create stereo images using pair of a simple web camera, raw data of an object positions are collected under realistic noisy conditions. The system has been tested using OpenCV and Matlab and the results of the experiments are presented here.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.34 no.3C
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• pp.274-278
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• 2009

Recently, the research which concerns the object class recognition has been done. Although an object tracking based on most of histograms employs a colored model to improve robustness, the system is not reliable enough yet. In this paper, we presents a method to express and track an object by using the histograms which are composed with visual features through successive frames. The experimental results shows that this method is reliable to track a car within 80m distance from camera.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.60 no.5
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• pp.1049-1054
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• 2011

Color is discerned due to the reflectance of object and its hue is determined by the Ratio of RGB spectrum. The aim of this study is to evaluate the Color information using by the optical system in terms of convenience and simplicity. Firstly, the differential Ratio of RGB is evaluated by photodiode Sensor which achieves a high efficiency with quick tranducer output. To extract the information of RGB Ratio, light emitting diode is built in Color sensing system with photodiode. Our proposed method is very economical, simple, and convenient. Our results can be applied to confirm the accurate information of Color in the research environments and commercial purposes. Thus RGB information can be determined subjectively and thus, the inherent characteristic of Color will be revealed precisely and its related features can be resolved in a real time mode.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.25 no.8B
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• pp.1413-1423
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• 2000

Color distribution is changed according to the variation of illumination position and illumination color. Therefore, even if images are relevant each other, retrieval accuracy is degraded. In this paper, we propose the image retrieval method using color information excluded illumination component. The proposed dynamic range control method removes the shadow region generated by change of illumination position to increase the color discrimination power. To exclude the illuminant color, we use the diffuse reflection component of object and gray world assumption. The experimental results show that the color histogram method using color information excluded illuminant has higher retrieval accuracy than conventional color histogram using the color information of input image.

• 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.

• Journal of Institute of Control, Robotics and Systems
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• v.12 no.8
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• pp.746-751
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• 2006

In this paper, we present tracking algorithm for the indoor moving object. We research passive method using a camera and image processing. It had been researched to use dynamic based estimators, such as Kalman Filter, Extended Kalman Filter and Particle Filter for tracking moving object. These algorithm have a good performance on real-time tracking, but they have a limit. If the shape of object is changed or object is located on complex background, they will fail to track them. This problem will need the complicated image processing algorithm. Finally, a large algorithm is made from integration of dynamic based estimator and image processing algorithm. For eliminating this inefficiency problem, image based estimator, Mean-shift Algorithm is suggested. This algorithm is implemented by color histogram. In other words, it decide coordinate of object's center from using probability density of histogram in image. Although shape is changed, this is not disturbed by complex background and can track object. This paper shows the results in real camera system, and decides 3D coordinate using the data from mean-shift algorithm and relationship of real frame and camera frame.