• Journal of KIISE:Software and Applications
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• v.32 no.11
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• pp.1121-1135
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• 2005

This paper proposes a moving object extraction method using the contrast map and salient points. In order to make the contrast map, we generate three-feature maps such as luminance map, color map and directional map and extract salient points from an image. By using these features, we can decide the Attention Window(AW) location easily The purpose of the AW is to remove the useless regions in the image such as background as well as to reduce the amount of image processing. To create the exact location and flexible size of the AW, we use motion feature instead of pre-assumptions or heuristic parameters. After determining of the AW, we find the difference of edge to inner area from the AW. Then, we can extract horizontal candidate region and vortical candidate region. After finding both horizontal and vertical candidates, intersection regions through logical AND operation are further processed by morphological operations. The proposed algorithm has been applied to many video sequences which have static background like surveillance type of video sequences. The moving object was quite well segmented with accurate boundaries.

• Journal of the Korea Society of Computer and Information
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• v.18 no.10
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• pp.1-12
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• 2013

This paper explores design space of many-core processors for a fire feature extraction algorithm. This paper evaluates the impact of varying the number of cores and memory sizes for the many-core processor and identifies an optimal many-core processor in terms of performance, energy efficiency, and area efficiency. In this study, we utilized 90 samples with dimensions of $256{\times}256$ (60 samples containing fire and 30 samples containing non-fire) for experiments. Experimental results using six different many-core architectures (PEs=16, 64, 256, 1,024, 4,096, and 16,384) and the feature extraction algorithm of fire indicate that the highest area efficiency and energy efficiency are achieved at PEs=1,024 and 4,096, respectively, for all fire/non-fire containing movies. In addition, all the six many-core processors satisfy the real-time requirement of 30 frames-per-second (30 fps) for the algorithm.

• Journal of the Korea institute for structural maintenance and inspection
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• v.28 no.1
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• pp.82-89
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• 2024

Black ice is very difficult to recognize and reduces the friction of the road surface, causing automobile accidents. Since black ice is difficult to detect, there is a need for a system that identifies black ice in real time and warns the driver. Various studies have been conducted to prevent black ice on road surfaces, but there is a lack of research on systems that identify black ice in real time and warn drivers. In this paper, an real-time image-based analysis system was developed to identify the condition of asphalt road surface, which is widely used in Korea. For this purpose, a dataset was built for each asphalt road surface image, and then the road surface condition was identified as dry, wet, black ice, and snow using deep learning. In addition, temperature and humidity data measured on the actual road surface were used to finalize the road surface condition. When the road surface was determined to be black ice, the salt spray equipment installed on the road was automatically activated. The surface condition recognition system for the asphalt concrete pavement and black ice automatic prevention system developed in this study are expected to ensure safe driving and reduce the incidence of traffic accidents.

• Journal of Broadcast Engineering
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• v.22 no.3
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• pp.295-312
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• 2017

Digital video content is the most information-intensive and high-value content. Therefore, it is necessary to protect the intellectual property rights of these contents, and this paper also proposes a watermarking method of digital image for this purpose. The proposed method uses the frequency characteristics of 2-Dimensional Discrete Wavelet Transform (2D-DWT) for digital images and digital watermark on global data without using local or specific data of the image for watermark embedding. The method to insert digital watermark data uses a simple Quantization Index Modulation (QIM) and a multiple watermarking method that inserts the same watermark data in multiple. When extracting a watermark, multiple watermarks are extracted and the final watermark data is determined by a simple statistical method. This method is an empirical method for experimentally determining the parameters in the watermark embedding process. The proposed method performs experiments on various images against various attacks and shows the superiority of the proposed method by comparing the performance with the representative existing methods.

• KIPS Transactions on Software and Data Engineering
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• v.2 no.1
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• pp.55-64
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• 2013

Due to the popularization of digital high-performance capturing equipments and the emergence of powerful image-editing softwares, it is easy for anyone to make a high-quality counterfeit money. However, the probability of detecting a counterfeit money to the general public is extremely low. In this paper, we propose a counterfeit money detection algorithm using a general purpose scanner. This algorithm determines counterfeit money based on the different features in the printing process. After the non-local mean value is used to analyze the noises from each money, we extract statistical features from these noises by calculating a gray level co-occurrence matrix. Then, these features are applied to train and test the support vector machine classifier for identifying either original or counterfeit money. In the experiment, we use total 324 images of original money and counterfeit money. Also, we compare with noise features from previous researches using wiener filter and discrete wavelet transform. The accuracy of the algorithm for identifying counterfeit money was over 94%. Also, the accuracy for identifying the printing source was over 93%. The presented algorithm performs better than previous researches.