• Journal of Korea Multimedia Society
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• v.1 no.2
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• pp.173-182
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• 1998

As a result of remarkable developments in multimedia technology, the image database system that can efficiently retrieve image data becomes a core technology of information-oriented society. In this paper, we proposed the 2-phase Image Retrieval System considering both color and shape information as the method of image features extraction for content-based image data retrieval. At the first level, to get color information, with improving and extending the indexing method using color distribution characteristic suggested by Striker et al., i.e. the indexing method considering local color distribution characteristics, the system roughly classifies images through the improved method. At the second level, the system finally retrieves the most similar image from the image queried by the user using the shape information about the image groups classified at the first level. To extract the shape information, we use the Improved Moment Invariants (IMI) that manipulates only the pixels on the edges of objects in order to overcome two main problems of the existing Moment Invariant methods large amount of processing and rotation sensitiveness which can frequently be seen in the Directive Histogram Intersection technique suggested by Jain et al. Experiments have been conducted on 300 automobile images. And we could obtain the more improved results through the comparative test with other methods.

• Journal of KIISE:Software and Applications
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• v.34 no.10
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• pp.937-944
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• 2007

In this paper, we propose multiple human tracking with an moving stereo camera. The tracking process is based on mean shift algorithm which is using color information of the target. Color based tracking approach is invariant to translation and rotation of the target but, it has several problems. Because of mean shift uses color distribution, it is sensitive to color distribution of background and targets. In order to solve this problem, we combine color and depth information of target. Also, we build human body part model to handle occlusions and we have created adaptive box scale. As a result, the proposed method is simple and efficient to track multiple humans in real time.

• Journal of Broadcast Engineering
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• v.5 no.1
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• pp.50-60
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• 2000

In this paper, we propose a new composite feature consists of both color and shape information that are suitable for the task of character image retrieval. This approach extracts shape-based information using Zernike moments from Y image in YCbCr color space. Zernike moments can extract shape-based features that are invariant to rotation, translation, and scaling. We also extract color-based information from the DCT coefficients of Cr and Cb image. This approach is good method reflecting human visual property and is suitable for web application such as large image database system and animation because higher retrieval rate has been achieved using only 36 features. In experiment, this method is applied to 3,834 character images. We confirmed that this approach brought about excellent effect by ANMRR(Average of Normalized, Modified Retrieval Rank), which is used in the evaluation measure of MPEG-7 color descriptor and BEP(Bull's Eye Performance), which is used in evaluation measure of shape descriptor in character image retrieval.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.38 no.1
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• pp.104-115
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• 2001

A new content-based color image retrieval method integrating the features of the color adjacency and the gradient is proposed in this paper. As the most used feature of color image, color histogram has its own advantages that it is invariant to the changes in viewpoint and the rotation of the image etc., and the computation of the feature is simple and fast. However, it is difficult to distinguish those different images having similar color distributions using histogram-based image retrieval, because the color histogram is generated on uniformly quantized colors and the histogram itself contains no spatial information. And another shortcoming of the histogram-based image retrieval is the storage of the features is usually very large. In order to prevent the above drawbacks, the gradient that is the largest color difference of neighboring pixels is calculated in the proposed method instead of the uniform quantization which is commonly used at most histogram-based methods. And the color adjacency information which indicates major color composition feature of an image is extracted and represented as a binary form to reduce the amount of feature storage. The two features are integrated to allow the retrieval more robust to the changes of various external conditions.

• The Transactions of the Korea Information Processing Society
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• v.5 no.11
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• pp.2776-2785
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• 1998

An image retrieval system based on image content is a key issue for building and managing large multimedia database, such as art galleries and museums, trademarks and copyrights, and picture archiving and communication system. Therefore, the interest on the subject of content-based image retrieval has been greatly increased for the last few years. This paper proposes a feature-based image retrieval technique which uses a compound feature vector representing both of color and shape of an image. Color information for the feature vector is obtained using the algebraic moment of each pixel of an image based on the property of regional color distribution. Shape information for the feature vector is obtained using the Improved Moment Invariant(IMI) which reduces the quantity of computation and increases retrieval efficiency. In the preprocessing phase for extracting shape feature, we transform a color image into a gray image. Since we make use of the modified DCT algorithm, it is implemented easily and can extract contour in real time. As an experiment, we have compared our method with previous methods using a database consisting of 150 automobile images, and the results of the experiment have shown that our method has the better performance on retrieval effectiveness.

• Journal of Institute of Control, Robotics and Systems
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• v.7 no.9
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• pp.749-757
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• 2001

We present a simple artificial landmark model and robust landmark tracking algorithm for mobile robot localization. The landmark model, consisting of symmetric and repetitive color patches, produces color histograms that are invariant under the geometric and photometric distortions. A stochastic approach based on the CONDENSATION tracks the landmark model robustly even under the varying illumination conditions. After the landmark detection, relative position of the mobile robot to the landmark is calculated. Experimental results show that the proposed landmark model is effective and can be detected and tracked in a clustered scene robustly. With the tracked single landmark, we extract geometrical information than achieve accurate localization.

• Journal of the Institute of Convergence Signal Processing
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• v.6 no.1
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• pp.15-22
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• 2005

In this paper, we propose a face recognition system by using the CCD color image. We first get the face candidate image by using YCbCr color model and adaptive skin color information. And we use it initial curve of active contour model to extract face region. We use the Eye map and mouth map using color information for extracting facial feature from the face image. To obtain center point of Log-polar image, we use extracted facial feature from the face image. In order to obtain feature vectors, we use extracted coefficients from DCT and wavelet transform. To show the validity of the proposed method, we performed a face recognition using neural network with BP learning algorithm. Experimental results show that the proposed method is robuster with higher recogntion rate than the conventional method for the rotation and scale variant.

• Journal of Korea Multimedia Society
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• v.19 no.9
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• pp.1679-1689
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• 2016

Recently, due to the rapid development of multimedia technologies, as image data has been extensive and large-scaled, the problem of increasing the time needed to retrieve the desired image is gradually critical. Image retrieval system that allows users to quickly and accurately search for the desired image information has been researched for a long time. However, in the case of content-based image retrieval representative Color Histogram, Color Coherence Vectors (CCV), Scale Invariant Feature Transform (SIFT) used in sensitive to changes in brightness, rotation, there is a problem that can occur misrecognized division off the power. In this paper, in order to evaluate the video retrieval system proposed, no change in brightness, respectively 0°, 90°, 180°, 270° rotated brightness up based on the case of changing, when the brightness down the results were compared with the performance evaluation of the system is an average of about 2% to provide the difference in performance due to changes in brightness, color histogram is an average of about 12.5%, CCV is an average of about 12.25%, it appeared in the SIFT is an average of about 8.5%, Thus, the proposed system of the variation width of the smallest in average about 2%, was confirmed to be robust to changes in the brightness and rotation than the existing systems.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.53 no.1
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• pp.22-31
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• 2004

This paper deals with the dynamic hand gesture recognition based on computer vision using neural networks. This paper proposes a global search method and a local search method to recognize the hand gesture. The global search recognizes a hand among the hand candidates through the entire image search, and the local search recognizes and tracks only the hand through the block search. Dynamic hand gesture recognition method is based on the skin-color and shape analysis with the invariant moment and direction information. Starting point and ending point of the dynamic hand gesture are obtained from hand shape. Experiments have been conducted for hand extraction, hand recognition and dynamic hand gesture recognition. Experimental results show the validity of the proposed method.

• Proceedings of the IEEK Conference
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• 2000.09a
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• pp.375-378
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• 2000

In this paper, we propose a novo] content-based image retrieval system using both Hidden Markov Model(HMM) and an improved chain code. The Gaussian Mixture Model(GMM) is applied to statistically model a color information of the image, and Deterministic Annealing EM(DAEM) algorithm is employed to estimate the parameters of GMM. This result is used to segment the given image. We use an improved chain code, which is invariant to rotation, translation and scale, to extract the feature vectors of the shape for each image in the database. These are stored together in the database with each HMM whose parameters (A, B, $\pi$) are estimated by Baum-Welch algorithm. With respect to feature vector obtained in the same way from the query image, a occurring probability of each image is computed by using the forward algorithm of HMM. We use these probabilities for the image retrieval and present the highest similarity images based on these probabilities.