• Journal of the Korea Society of Computer and Information
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• v.4 no.3
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• pp.35-46
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• 1999

In this paper, we propose a new CPLD technology mapping algorithm improving run-time under time constraint. In our technology mapping algorithm. a given logic equation is constructed as the DAG type. then the DAG is reconstructed by replicating the node that outdegree is more than or equal to 2. As a result, it makes delay time and the number of CLBs, run-time to be minimized. Also. after the number of multi-level is defined and cost of each nodes is calculated, the graph is partitioned in order to fit to k that is the number of OR term within Cl.B. The partitioned nodes are merged through collapsing and bin packing is performed in order to fit to the number of OR term within CLB. In the results of experiments to MCNC circuits for logic synthesis benchmark, we can shows that proposed technology mapping algorithm reduces run-time much more than the TMCPLD.

• KIPS Transactions on Software and Data Engineering
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• v.1 no.2
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• pp.115-126
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• 2012

The artificial captions appearing in videos include information that relates to the videos. In order to obtain the information carried by captions, many methods for caption extraction from videos have been studied. Most traditional methods of detecting caption region have used one frame. However video include not only spatial information but also temporal information. So we propose a method of detection caption region using temporal and spatial information. First, we make improved Text-Appearance-Map and detect continuous candidate regions through matching between candidate-regions. Second, we detect disappearing captions using disappearance test in candidate regions. In case of captions disappear, the caption regions are decided by a merging process which use temporal and spatial information. Final, we decide final caption regions through ANNs using edge direction histograms for verification. Our proposed method was experienced on many kinds of captions with a variety of sizes, shapes, positions and the experiment result was evaluated through Recall and Precision.

• Journal of Digital Contents Society
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• v.1 no.1
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• pp.53-64
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• 2000

This paper is related to color image segmentation scheme which makes it possible to achieve the excellent segmented results by block-based segmentation using Y/C bit-plane summation image. First, normalized chrominance summation image is obtained by normalizing the image which is summed up the absolutes of color-differential values between R, G, B images. Secondly, upper 2 bits of the luminance image and upper 6bits of and the normalized chrominance summation image are bitwise operated by the pixel to generate the Y/C bit-plane summation image. Next, the Y/C bit-plane summation image divided into predetermined block size, is classified into monotone blocks, texture blocks and edge blocks, and then each classified block is merged to the regions including one more blocks in the individual block type, and each region is selectively allocated to unique marker according to predetermined marker allocation rules. Finally, fine segmented results are obtained by applying the watershed algorithm to each pixel in the unmarked blocks. As shown in computer simulation, the main advantage of the proposed method is that it suppresses the over-segmentation in the texture regions and reduces computational load. Furthermore, it is able to apply global parameters to various images with different pixel distribution properties because they are nonsensitive for pixel distribution. Especially, the proposed method offers reasonable segmentation results in edge areas with lower contrast owing to the regional characteristics of the color components reflected in the Y/C bit-plane summation image.

• Journal of KIISE:Software and Applications
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• v.31 no.4
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• pp.420-438
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• 2004

Artificially or naturally contained texts in the natural images have significant and detailed information about the scenes. If we develop a method that can extract and recognize those texts in real-time, the method can be applied to many important applications. In this paper, we suggest a new method that extracts the text areas in the natural images using the low-level image features of color continuity. gray-level variation and color valiance and that verifies the extracted candidate regions by using the high-level text feature such as stroke. And the two level features are combined hierarchically. The color continuity is used since most of the characters in the same text lesion have the same color, and the gray-level variation is used since the text strokes are distinctive in their gray-values to the background. Also, the color variance is used since the text strokes are distinctive in their gray-values to the background, and this value is more sensitive than the gray-level variations. The text level stroke features are extracted using a multi-resolution wavelet transforms on the local image areas and the feature vectors are input to a SVM(Support Vector Machine) classifier for the verification. We have tested the proposed method using various kinds of the natural images and have confirmed that the extraction rates are very high even in complex background images.