• Proceedings of the KIEE Conference
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• 2008.07a
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• pp.1819-1820
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• 2008

Steel slabs are marked with slab management numbers (SMNs). To increase efficiency, automated identification of SMNs from digital images is desirable. Automatic extraction of SMNs is a prerequisite for automatic character segmentation and recognition. The images include complex background, and the position of the text region of the slabs is variable. This paper describes an pre-processing algorithm for detection of slab information using robust feature points extraction. Using SIFT(Scale Invariant Feature Transform) algorithm, we can reduce the search region for extraction of SMNs from the slab image.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2009.01a
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• pp.99-102
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• 2009

In this paper a hierarchical stereo matching algorithm based on feature extraction is proposed. The boundary (edge) as feature point in an image is first obtained by segmenting an image into red, green, blue and white regions. With the obtained boundary information, disparities are extracted by matching window on the image boundary, and the initial disparity map is generated when assigned the same disparity to neighbor pixels. The final disparity map is created with the initial disparity. The regions with the same initial disparity are classified into the regions with the same color and we search the disparity again in each region with the same color by changing block size and search range. The experiment results are evaluated on the Middlebury data set and it show that the proposed algorithm performed better than a phase based algorithm in the sense that only about 14% of the disparities for the entire image are inaccurate in the final disparity map. Furthermore, it was verified that the boundary of each region with the same disparity was clearly distinguished.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.57 no.12
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• pp.2281-2284
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• 2008

The purpose of this paper is to develope the drowsiness-drive perception system which judges drowsiness driving based on drivers' eye region using single vision system. To do this, first, we use the Haar-like feature and AdaBoost learning algorithm for detecting the features of the face region. And we measure the eye blinking frequency and eye closure duration from these feature data. And then, we propose the drowsiness-drive detection algorithm using the eye blinking frequency and eye closure duration. Finally, we have shown the effectiveness and feasibility of the proposed method through some experiments.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.8 no.6
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• pp.1524-1529
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• 2007

In this paper, automatic shot panorama construction method is presented. For construction of panorama image, conventional panoramic techniques manually took two panorama members, but the proposed method automatically takes panorama members according to moving camera and constructs panorama image. The panorama members are automatically selected and taken by tracking region over image stream form camera. Matching region for panorama including the tracking region in the members is selected and applied by invariant feature panoramic method. Our method can automatically shot panorama members and has merit of high processing speed. In the experiments, it was shown that the algorithm required about 0.89 second in processing time, about two times shorter than existing invariant feature based one(6), for color images of $320{\times}240$ size.

• Journal of Korea Multimedia Society
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• v.6 no.2
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• pp.240-246
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• 2003

In this paper, we propose the fast image retrieval method based on object regions using bidirectional round filter in the wavelet transform region. A conventional method that extracts feature vectors on the whole of subband is reduced retrieval efficiency, because of unnecessary background information. The proposed method that extracts feature vectors on the only object region of subband by using bidirectional round filter improve retrieval efficiency, because of removing of background information. And it certainly maintains retrieval efficiency in case of reduction of feature vectors according to color information. Consequently, the retrieval efficiency is improved with 2.5%∼5.3% values, which have a little changes according to characteristics of image.

• International Journal of Contents
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• v.8 no.1
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• pp.30-38
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• 2012

Text extraction and binarization are the important pre-processing steps for text recognition. The performance of text binarization strongly related to the accuracy of recognition stage. In our proposed method, the first stage based on line detection and shape feature analysis applied to locate the position of a business card and detect the shape from the complex environment. In the second stage, several local regions contained the possible text components are separated based on the projection histogram. In each local region, the pixels grouped into several connected components based on the connected component labeling and projection histogram. Then, classify each connect component into text region and reject the non-text region based on the feature information analysis such as size of connected component and stroke width estimation.

• Journal of the Korean Society of Combustion
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• v.18 no.3
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• pp.31-37
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• 2013

Numerical simulations are conducted to investigate the feature of spontaneous ignition of hydrogen within a certain length of downstream tube released by the failure of pressure boundaries of various geometric assumption. The results show that the ignition feature can be varied with the shape of pressure boundary. The ignition at the contact region are developed at the spherical pressure boundaries due to multi-dimensional shock interactions, whereas the local ignition is developed in limited area such as boundary layer at the planar pressure boundary conditions. The spontaneous ignition inside the tube can be generated from the reaction region of only boundary layer regardless of existence of the reaction of core region.

• Journal of Korea Multimedia Society
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• v.13 no.4
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• pp.504-511
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• 2010

In this paper, we propose the face region recognition system for City-Security CCTV(Closed Circuit Television) using hippocampal neural network which is modelling of human brain's hippocampus. This system is composed of feature extraction, learning and recognition part. The feature extraction part is constructed using PCA(Principal Component Analysis) and LDA(Linear Discriminants Analysis). In the learning part, it can label the features of the image-data which are inputted according to the order of hippocampal neuron structure to reaction-pattern according to the adjustment of a good impression in a dentate gyrus and remove the noise through the auto-associative memory in the CA3 region. In the CA1 region receiving the information of the CA3, it can make long-term memory learned by neuron. Experiments confirm the each recognition rate, that are shape change and light change. The experimental results show that we can compare a feature extraction and learning method proposed in this paper of any other methods, and we can confirm that the proposed method is superior to existing methods.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.19 no.4
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• pp.940-947
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• 2015

This paper presents the region of interest pattern image extraction method on a display printed matrix pattern. Proposed method can not use conventional method such as laser, ultrasonic waves and touch sensor. It searches feature point and rotation angle using luminance and pattern reliable feature points of input image, and then it extracts region of interest. In order to extract region of interest, we simulate proposed method using pattern image written various angles on display panel. The proposed method makes progress using the OpenCV and the window program, and was designed using Verilog-HDL and was verified through the FPGA Board(xc6vlx760) of Xilinx.

• The Journal of the Korea Contents Association
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• v.6 no.2
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• pp.44-50
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• 2006

This study has proposed a method of content-based image retrieval using object region in order to overcome disadvantages of existing color histogram methods. The existing color histogram methods have a weak point of reducing accuracy, because these have both a quantization error and an absence of spatial information. In order to overcome this problem, we convert a color information to a HSV space, quantize hue factor being pure color information, and calculate histogram. And then we use hue for retrieval feature that is robust in brightness, movement, and rotation. To solve the problem of the absence of spatial information, we select object region in terms of color feature and region correlation. And we use both the edge and the DC in the selected region for retrieving. As a result of experiment with 1,000 natural color images, the proposed method shows better precision and recall than the existing methods.