• Journal of the Korea Institute of Information and Communication Engineering
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• v.10 no.3
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• pp.517-525
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• 2006

In this paper, the algorithm which improve the efficiency of surveillance in spite of the change of light is proposed and confirmed by virtue of the experiments. One of the problems for the implementation of visual surveillance system is the image processing technique to overcome with the variations of illumination lights. Some conventional systems are generally not considered the error due to the change of lights because the system use at indoor. In practical, the factors of bad image can be classified to the ghosts due to the reflection of lights and shadows in a scene. Especially weak images and noises at night are decreased the performance of visual surveillance system. In the paper, the filter which improve the images with some change of illumination lights is designed and the gabor filter is used for recognition and tracking of the moving objects. In the results, the system showed that the recognition and tracking were obtained $92\sim100%$ of recognition rate at daytime, but $80\sim90%$ of nighttime.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.42 no.2 s.302
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• pp.121-130
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• 2005

In this paper, we propose a feature extraction method for iris recognition using wavelet transforms. The wavelet transform is fast and has a good localization characteristic. In particular, the low frequency band can be used as an effective feature vector. In iris recognition, the noise caused by eyelid the eyebrow, glint, etc may be included in iris. The iris pattern is distorted by noises by itself, and a feature extraction algorithm based on filter such as Wavelets, Gabor transform spreads noises into whole iris region. Namely, such noises degrade the performance of iris recognition systems a major problem. This kind of noise has adverse effect on performance. In order to solve these problems, we propose to divide the iris image into a number of sub-region and apply the wavelet transform to each sub-region. Experimental results show that the performance of proposed method is comparable to existing methods using Gabor transform and region division noticeably improves recognition performance. However, it is noted that the processing time of the wavelet transform is much faster than that of the existing methods.

• Journal of the Institute of Convergence Signal Processing
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• v.11 no.3
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• pp.215-220
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• 2010

In this paper, we propose a new texture-based page segmentation and classification method in which table region, background region, image region and text region in a given document image are automatically identified. The proposed method for document images consists of two stages, document segmentation and contents classification. In the first stage, we segment the document image, and then, we classify contents of document in the second stage. The proposed classification method is based on a texture analysis. Each contents in the document are considered as regions with different textures. Thus the problem of classification contents of document can be posed as a texture segmentation and analysis problem. Two-dimensional Gabor filters are used to extract texture features for each of these regions. Our method does not assume any a priori knowledge about content or language of the document. As we can see experiment results, our method gives good performance in document segmentation and contents classification. The proposed system is expected to apply such as multimedia data searching, real-time image processing.